https://github.com/JialeCao001/PedSurvey

From Handcrafted to Deep Features for Pedestrian Detection: A Survey (TPAMI 2021)
https://github.com/JialeCao001/PedSurvey
multispectral-pedestrian-detection pedestrian-detection survey
Last synced: 3 months ago
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From Handcrafted to Deep Features for Pedestrian Detection: A Survey (TPAMI 2021)
Host: GitHub
URL: https://github.com/JialeCao001/PedSurvey
Owner: JialeCao001
Created: 2020-08-13T16:41:50.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-08-18T12:14:38.000Z (almost 2 years ago)
Last Synced: 2024-08-01T03:32:39.525Z (11 months ago)
Topics: multispectral-pedestrian-detection, pedestrian-detection, survey
Homepage: https://arxiv.org/pdf/2010.00456.pdf
Size: 1.05 MB
Stars: 168
Watchers: 13
Forks: 28
Open Issues: 1
Metadata Files:
- Readme: README.md
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README

        ## From Handcrafted to Deep Features for Pedestrian Detection: A Survey

This project provides a paper list about pedestrian detection following the taxonomy in "*[From Handcrafted to Deep Features for Pedestrian Detection: A Survey

(IEEE TPAMI 2022)](https://arxiv.org/pdf/2010.00456.pdf)*". 

- **Single-spectral pedestrian detection** and **multispectral pedestrian detection** are both summarized.

- The performance of some methods on different datasets are shown in [Leaderboard](Comparison.md).

- We release a new large-scae pedestrian detection dataset **TJU-DHD-Pedestrian**: [arXiv2021](https://arxiv.org/pdf/2011.09170.pdf), [TIP2021](https://ieeexplore.ieee.org/document/9247499), [website](https://github.com/tjubiit/TJU-DHD), [Learderboard](https://paperswithcode.com/dataset/tju-dhd)

- If you find a new paper or an error, please feel free to contact us.  

    

         

         

    

    

         

    

## News

```

**PD**: Pedestrian Detection; **MPD**: Multispectral Pedestrian Detection; **MVD**: Multi-View Pedestrian Detection; **Others**: Pedestrian Detection with Special Devices

```

- [Aug. 18, 2023]: **PD**: [OTP-NMS(TIP2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10130101), [Seq2SeqNMS(TIP2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10107719), [MsSE-SR(TITS2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10168965), [CFRLA-Net(TCSVT2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10045738); **MPD**: [MCHE-CF(TMM2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10114594); **Others**: [PEDRo(CVPRW2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10208992), [NIRPed(TITS2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10077447)

- [June 9, 2023]: **PD**: [OPL(CVPR2023)](https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.pdf), [LSFM(CVPR2023)](https://openaccess.thecvf.com/content/CVPR2023/papers/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.pdf), [VLPD(CVPR2023)](https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.pdf); 

- [April 16, 2023]: **PD**: [CTD(TMM2023)](https://ieeexplore.ieee.org/abstract/document/10057083), [DDAD(TITS2023)](https://ieeexplore.ieee.org/abstract/document/9963778), [MMPD-MDCNN(IF2023)](https://www.sciencedirect.com/science/article/pii/S1566253523000544), [DINF(arXiv2023)](https://arxiv.org/pdf/2301.05565.pdf); **Others**: [PiFeNet(RAL2023)](https://ieeexplore.ieee.org/abstract/document/10003992)

- [April 16, 2023]: We add Multi-View Pedestrian Detection (MVD) below and also present some works here.  **MVD**: [MVAug(WACV2023)](https://openaccess.thecvf.com/content/WACV2023/papers/Engilberge_Two-Level_Data_Augmentation_for_Calibrated_Multi-View_Detection_WACV_2023_paper.pdf), [KSMVD(PR2022)](https://www.sciencedirect.com/science/article/pii/S0031320322003478), [3DROM(ECCV2022)](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136700681.pdf), [SHOT(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Song_Stacked_Homography_Transformations_for_Multi-View_Pedestrian_Detection_ICCV_2021_paper.pdf), [MVDeTr(ACMMM2021)](https://dl.acm.org/doi/10.1145/3474085.3475310), [MVDet(ECCV2020)](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520001.pdf)

- [Dec. 31, 2022]: **PD**: [SMFE(NeurIPS2022)](https://openreview.net/forum?id=eow_ZGaw24j), [MB-CSP(TITS2022)](https://ieeexplore.ieee.org/abstract/document/9857630), [SMPD(Neurcomputing2022)](https://www.sciencedirect.com/science/article/pii/S0925231221018026), [IDADA(TIM2022)](https://ieeexplore.ieee.org/abstract/document/9781425), [YOLOv3-promote(TITS2022)](https://ieeexplore.ieee.org/abstract/document/9669164), [HQPFG(PR2022)](https://www.sciencedirect.com/science/article/pii/S0031320322000863), [SF-UPD(ICME2022)](https://ieeexplore.ieee.org/abstract/document/9859707), [IFDNN(TITS2022)](https://ieeexplore.ieee.org/abstract/document/9906846), [SWDR-SR(TIM2022)](https://ieeexplore.ieee.org/abstract/document/9678120), [Region NMS(Neurocomputing2022)](https://www.sciencedirect.com/science/article/pii/S0925231221014843), [IPOPD(arXiv2022)](https://arxiv.org/pdf/2205.04812.pdf); **MPD**: [DCMNet(ACMMM2022)](https://dl.acm.org/doi/abs/10.1145/3503161.3547895), [MICNN(CVIU2022)](https://www.sciencedirect.com/science/article/pii/S1077314222001321), [LG-FAPF(IF2022)](https://www.sciencedirect.com/science/article/pii/S1566253522000549), [UTVDA(PRL2022)](https://www.sciencedirect.com/science/article/pii/S0167865521004153); **Others**: [FSSPA(CVPR2022W)](https://openaccess.thecvf.com/content/CVPR2022W/L3D-IVU/html/Wiedemer_Few-Shot_Supervised_Prototype_Alignment_for_Pedestrian_Detection_on_Fisheye_Images_CVPRW_2022_paper.html)

- [May 10, 2022]: **PD**: [OAF-Net(TITS2022)](https://ieeexplore.ieee.org/document/9770474), [F2DNet(ICPR2022)](https://arxiv.org/abs/2203.02331), [Pedstron(arXiv2022)](https://arxiv.org/abs/2201.03176); **MPD**: [CMPD(TMM2022)](https://ieeexplore.ieee.org/document/9739079/); **Others**: [STCrowd(CVPR2022)](https://arxiv.org/abs/2204.01026),

- [Mar. 4, 2022]: **PD**: [DMSFLN(TITS2021)](https://ieeexplore.ieee.org/document/9507387), [SA-DPM(TITS2022)](https://ieeexplore.ieee.org/document/9694517), [SSC(TITS2022)](https://ieeexplore.ieee.org/document/9690771), [CFL(TITS2022)](https://ieeexplore.ieee.org/document/9690771); **MPD**: [UGCML(TCSVT2021)](https://ieeexplore.ieee.org/document/9419080/), [[MuFEm(TITS2022)]](https://arxiv.org/pdf/2105.12713.pdf), [BAANet(arXiv2021)](https://arxiv.org/abs/2112.02277); **Others**: [UAVPed(TMM2021)](https://ieeexplore.ieee.org/document/9417704), [RAHD(TMM2022)](https://arxiv.org/abs/2112.08743)

- [Dec. 5, 2021]: **PD**: [EGCL(arXiv2021)](https://arxiv.org/abs/2111.08974), [AutoPedestrian(TIP2021)](https://ieeexplore.ieee.org/document/9563123/), [SADet(IJCB2021)](https://ieeexplore.ieee.org/document/9484371), [PAMS-FCN(TITS2021)](https://ieeexplore.ieee.org/abstract/document/8960286), [SAN(TIP2021)](https://ieeexplore.ieee.org/document/9329171), [Un2Reliab(TMM2021)](https://ieeexplore.ieee.org/document/9352521)

- [Nov. 1, 2021]: **PD**: [CRML(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Robust_Small-Scale_Pedestrian_Detection_With_Cued_Recall_via_Memory_Learning_ICCV_2021_paper.pdf); **MPD**: [GAFF(WACV2021)](https://openaccess.thecvf.com/content/WACV2021/papers/Zhang_Guided_Attentive_Feature_Fusion_for_Multispectral_Pedestrian_Detection_WACV_2021_paper.pdf)

- [Aug. 31, 2021]: **Dataset**: [MOTSynth(ICCV2021)](https://arxiv.org/pdf/2108.09518.pdf), [LLVIP(ICCVW2021)](https://arxiv.org/pdf/2108.10831.pdf); **MPD**: [MRMIoU(MVA2021)](https://arxiv.org/pdf/2107.11196.pdf)

- [July 4, 2021]: **PD**: [NMS-Loss(ICMR2021)](https://arxiv.org/abs/2106.02426), [VPD(arXiv2021)](https://arxiv.org/abs/2104.12389); **MPD**: [SCDN(arXiv2021)](https://arxiv.org/abs/2105.12713); **Others**: [SBBG(CVIU2021)](https://arxiv.org/abs/2104.13764)

- [April 16, 2021]: **PD**: [LLA(arXiv2021)](https://arxiv.org/pdf/2101.04307.pdf), [Box Re-Ranking(arXiv2021)](https://arxiv.org/pdf/2102.00595.pdf), [V2F-Net(arXiv2021)](https://arxiv.org/pdf/2104.03106.pdf)

- [Mar. 19, 2021]: **PD**: [DRNet(arXiv2021)](https://arxiv.org/pdf/2103.10091.pdf)

- [Jan. 07, 2021]: **PD**: [DETR for Pedestrian Detection(arXiv2020)](https://arxiv.org/abs/2012.06785)

- [Dec. 05, 2020]: **PD**: [KGSNet(TNNLS2020)](https://ieeexplore.ieee.org/document/9137724), [SSAM(TITS2020)](https://ieeexplore.ieee.org/document/9186837), [MGAN(TIP2020)](https://ieeexplore.ieee.org/document/9282190), [PEN(TITS2020)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9109304), [RSA-YOLO(TIP2020)](https://ieeexplore.ieee.org/document/9272650), [CWETM(TVT2020)](https://ieeexplore.ieee.org/document/9050835), [PLM(TITS2020)](https://ieeexplore.ieee.org/document/8701617), [GRPN(TITS2020)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9091903); **Others**: [SSD-MR(ICRA2020)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9197399), [ADGN(TITS2020)](https://ieeexplore.ieee.org/document/9145843)

- [Nov. 19, 2020]: **Dataset**: A newly built deverse pedestrian detection dataset: [TJU-DHD-Pedestrian(TIP2020)](https://arxiv.org/pdf/2011.09170.pdf) 

- [Nov. 05, 2020]: **PD**: [TinyCityPersons(WACV2021)](https://arxiv.org/pdf/2011.02298.pdf)

- [Oct. 22, 2020]: **PD**: [BGCNet(ACM-MM2020)](https://dl.acm.org/doi/pdf/10.1145/3394171.3413989), [NOH-NMS(ACM-MM2020)](https://arxiv.org/pdf/2007.13376.pdf), [SML(ACM-MM2020)](https://cse.buffalo.edu/~jsyuan/papers/2020/SML.pdf), [HGPD(ACM-MM2020)](https://dl.acm.org/doi/pdf/10.1145/3394171.3413983)

- [Oct. 07, 2020]: Comparison of multispectral pedestrian detection in leaderboard

- [Oct. 01, 2020]: **PD**: [PRNet(ECCV2020)](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680035.pdf), [Case(ECCV2020)](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620086.pdf); **MPD**: [MBNet(ECCV2020)](https://arxiv.org/abs/2008.03043.pdf); **Others**: [TCDet(ECCV2020)](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670545.pdf); 

## Table of Contents

1. [Detection pipeline](#1)  

    1.1 [Proposal generation](#1.1)  

    1.2 [Feature extraction](#1.2)   

    1.3 [Proposal classification](#1.3)  

    1.4 [Post processing](#1.4)  

2. [Single-spectral pedestrian detection](#2)  

    2.1 [Handcrafted features based pedestrian detection](#2.1)  

         *2.1.1 [Channel features based methods](#2.1.1)*    

         *2.1.2 [Deformable part based methods](#2.1.2)*  

    2.2 [Deep features based pedestrian detection](#2.2)  

         *2.2.1 [Hybrid methods](#2.2.1)*  

         *2.2.2 [Pure CNN based methods](#2.2.2)*

3. [Multispectral pedestrian detection](#3)  

    3.1 [Deep feature fusion](#3.1)  

    3.2 [Data processing](#3.2)  

    3.3 [Domain adaptation](#3.3)     

4. [Datasets](#4)  

    4.1 [Earlier pedestrian datasets](#4.1)  

    4.2 [Modern pedestrian datasets](#4.2)  

    4.3 [Multispectral pedestrian datasets](#4.3)  

5. [Challenges](#5)  

    5.1 [Scale variance](#5.1)  

    5.2 [Occlusion](#5.2)  

    5.3 [Domain adaptation](#5.3)  

6. [Related Survey](#6) 

7. [Multi-View Pedestrian Detection](#9)  

8. [Leaderboard](Comparison.md)

9. [Citation](#8)

## 1. Detection pipeline 

- **1.1. Proposal generation** 

  - **Sliding windows**

  - **Particle windows**

    - Multistage particle windows for fast and accurate object detection, PAMI 2011. [[Paper]](https://ieeexplore.ieee.org/document/6109271)

    - Learning sampling distributions for efficient object detection, TIP 2017. [[Paper]](https://arxiv.org/pdf/1508.05581.pdf)

  - **Objectness methods**

    - Edge boxes: Locating object proposals from edges, ECCV 2014. [[Paper]](https://pdollar.github.io/files/papers/ZitnickDollarECCV14edgeBoxes.pdf)

    - Bing: Binarized normed gradients for objectness estimation at 300fps, CVPR 2014. [[Paper]](http://www.robots.ox.ac.uk/~tvg/publications/2019/Cheng_BING_Binarized_Normed_2014_CVPR_paper.pdf)

    - What makes for effective detection proposals, PAMI 2016. [[Paper]](https://arxiv.org/pdf/1502.05082.pdf)

    - Selective search for object recognition, IJCV 2016. [[Paper]](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)

  - **Region proposal networks**  

    - Faster rcnn: Towards real-time object detection with region proposal networks, NIPS 2015. [[Paper]](https://arxiv.org/pdf/1506.01497.pdf)

    - A unified multi-scale deep convolutional neural network for fast object detection, ECCV 2016. [[Paper]](https://arxiv.org/pdf/1607.07155.pdf)

    - Region proposal by guided anchoring, CVPR 2019. [[Paper]](https://arxiv.org/pdf/1901.03278.pdf)

- **1.2. Feature extraction**  

  - **Handcrafted features**

    - Robust real-time face detection, IJCV 2004. [[Paper]](https://www.face-rec.org/algorithms/Boosting-Ensemble/16981346.pdf)

    - Histograms of oriented gradients for human detection, CVPR 2005. [[Paper]](http://vision.stanford.edu/teaching/cs231b_spring1213/papers/CVPR05_DalalTriggs.pdf)

    - Integral channel features, BMVC 2009. [[Paper]](https://pdollar.github.io/files/papers/DollarBMVC09ChnFtrsAbstract.pdf)

    - Object detection with discriminatively trained partbased models, CVPR 2009/PAMI 2010. [[Paper]](http://cs.brown.edu/people/pfelzens/papers/lsvm-pami.pdf)

  - **Deep features**

    - Imagenet classification with deep convolutional neural networks, NIPS 2012. [[Paper]](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)

    - Very deep convolutional networks for large-scale image recognition, arXiv 2014. [[Paper]](https://arxiv.org/pdf/1409.1556.pdf)

    - Deep residual learning for image recognition, CVPR 2016. [[Paper]](https://arxiv.org/pdf/1512.03385.pdf)

    - Densely connected convolutional networks, CVPR 2017. [[Paper]](https://arxiv.org/pdf/1608.06993.pdf)

- **1.3. Proposal classification/regression**  

    - Support-vector networks, ML 1995. [[Paper]](https://link.springer.com/content/pdf/10.1023%2FA%3A1022627411411.pdf)

    - A decision-theoretic generalization of on-line learning and an application to boosting, JCSS 1997. [[Paper]](http://cseweb.ucsd.edu/~yfreund/papers/adaboost.pdf)

    - Softmax layer, Sigmoid layer, Smooth L1 layer     

- **1.4. Post processing**  

    - Greedy NMS

    - Soft-nms–improving object detection with one line of code, ICCV 2017. [[Paper]](https://arxiv.org/pdf/1704.04503.pdf)

    - Learning nonmaximum suppression, CVPR 2017. [[Paper]](https://arxiv.org/pdf/1705.02950.pdf)

    - Relation networks for object detection, CVPR 2018. [[Paper]](https://arxiv.org/pdf/1711.11575.pdf)

    - Learning to separate: Detecting heavily-occluded objects in urban scenes, arXiv 2019. [[Paper]](https://arxiv.org/pdf/1912.01674.pdf)

    - Adaptive nms: Refining pedestrian detection in a crowd, CVPR 2019. [[Paper]](https://arxiv.org/pdf/1904.03629.pdf)

   

## 2. Single-spectral pedestrian detection 

#### 2.1. Handcrafted features based pedestrian detection 

- **2.1.1. Channel features based methods**  

  - Robust real-time face detection, IJCV 2004. [[Paper]](https://www.face-rec.org/algorithms/Boosting-Ensemble/16981346.pdf)

  - Integral channel features, BMVC 2009. [[Paper]](https://pdollar.github.io/files/papers/DollarBMVC09ChnFtrsAbstract.pdf)

  - New features and insights for pedestrian detection, CVPR 2010. [[Paper]](https://ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/walk10cvpr.pdf)

  - Fast feature pyramids for object detection, BMVC 2010/PAMI 2014. [[Paper]](https://vision.cornell.edu/se3/wp-content/uploads/2014/09/DollarPAMI14pyramids_0.pdf)

  - Crosstalk cascades for frame-rate pedestrian detection, ECCV 2012. [[Paper]](http://www.vision.caltech.edu/publications/DollarECCV12crosstalkCascades.pdf)

  - Seeking the strongest rigid detector, CVPR 2013. [[Paper]](https://rodrigob.github.io/documents/2013_cvpr_roerei_with_supplementary_material.pdf)

  - Exploring weak stabilization for motion feature extraction, CVPR 2013. [[Paper]](https://www.cs.cmu.edu/~deva/papers/motionftrs.pdf)

  - Informed haar-like features improve pedestrian detection, CVPR 2014. [[Paper]](https://ieeexplore.ieee.org/document/6909521)

  - Local decorrelation for improved pedestrian detection, NIPS 2014. [[Paper]](https://papers.nips.cc/paper/5419-local-decorrelation-for-improved-pedestrian-detection.pdf)

  - Exploring human vision driven features for pedestrian detection, TCSVT 2015. [[Paper]](https://ieeexplore.ieee.org/document/7027791)

  - Filtered channel features for pedestrian detection, CVPR 2015. [[Paper]](https://arxiv.org/abs/1501.05759.pdf)

  - Looking at pedestrians at different scales: A multiresolution approach and evaluations, TITS 2016. [[Paper]](https://eshed1.github.io/papers/Multires_Peds.pdf)

  - Semantic channels for fast pedestrian detection, CVPR 2016. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2016/papers/Costea_Semantic_Channels_for_CVPR_2016_paper.pdf)

  - How far are we from solving pedestrian detection? CVPR 2016. [[Paper]](https://arxiv.org/pdf/1602.01237.pdf)

  - Pedestrian detection inspired by appearance constancy and shape symmetry, CVPR 2016/TIP 2016. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2016/papers/Cao_Pedestrian_Detection_Inspired_CVPR_2016_paper.pdf)

  - Pedestrian detection with spatially pooled features and structured ensemble learning, ECCV 2016/PAMI 2017. [[Paper]](https://arxiv.org/pdf/1409.5209.pdf)

  - Discriminative latent semantic feature learning for pedestrian detection, Neurocomputing 2017. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231217301170)

  - A novel pixel neighborhood differential statistic feature for pedestrian and face detection, PR 2017. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0031320316302710)

  - Local co-occurrence selection via partial least squares for pedestrian detection, TITS 2017. [[Paper]](https://ieeexplore.ieee.org/document/7589013)

  - Fast boosting based detection using scale invariant multimodal multiresolution filtered features, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Costea_Fast_Boosting_Based_CVPR_2017_paper.pdf)

  - Pedestrian detection by feature selected self-similarity features, IEEE Access 2018. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8286891)

  - An extended filtered channel framework for pedestrian detection, TITS 2018. [[Paper]](https://ieeexplore.ieee.org/document/8310009)

  - Lbp channels for pedestrian detection, WACV 2018. [[Paper]](https://hal.inria.fr/hal-01849431/document)

  - Pedestrian proposal and refining based on the shared pixel differential feature, TITS 2019. [[Paper]](https://ieeexplore.ieee.org/document/8443439)

  - Group cost-sensitive boostlr with vector form decorrelated filters for pedestrian detection, TITS 2019. [[Paper]](https://ieeexplore.ieee.org/document/8880687/)

  - Pedestrian detection using pixel difference matrix projection, TITS 2020. [[paper]](https://ieeexplore.ieee.org/document/8703888)

- **2.1.2. Deformable part based methods**  

  - Histograms of oriented gradients for human detection, CVPR 2005. [[Paper]](https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf)

  - Object detection with discriminatively trained partbased models, CVPR 2009/TPAMI 2010. [[Paper]](http://cs.brown.edu/people/pfelzens/papers/lsvm-pami.pdf)

  - Cascade object detection with deformable part models, CVPR 2010. [[Paper]](http://rogerioferis.com/VisualRecognitionAndSearch2013/material/Class4DPM2.pdf)

  - Multiresolution models for object detection, ECCV 2010. [[Paper]](https://vision.ics.uci.edu/papers/ParkRF_ECCV_2010/ParkRF_ECCV_2010.pdf)

  - Robust multi-resolution pedestrian detection in traffic scenes, CVPR 2013. [[Paper]](https://yan-junjie.github.io/publication/dblp-confcvpr-yan-zlll-13/dblp-confcvpr-yan-zlll-13.pdf)

  - Single-pedestrian detection aided by multi-pedestrian detection, CVPR 2013/TPAMI 2015. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2013/papers/Ouyang_Single-Pedestrian_Detection_Aided_2013_CVPR_paper.pdf)

  - Regionlets for generic object detection, CVPR 2013/TPAMI 2015. [[Paper]](http://users.eecs.northwestern.edu/~mya671/mypapers/ICCV13_Wang_Yang_Zhu_Lin.pdf)

  - Pedestrian detection in crowded scenes via scale and occlusion analysis, ICIP 2016. [[paper]](https://faculty.ucmerced.edu/mhyang/papers/icip16_mot.pdf)

  - Real-time rgb-d based template matching pedestrian detection, ICRA 2016. [[paper]](https://arxiv.org/pdf/1610.00748.pdf)

  - A pedestrian detection system accelerated by kernelized proposals, TITS 2020. [[paper]](https://ieeexplore.ieee.org/document/8681730)

#### 2.2. Deep features based pedestrian detection 

- **Hybrid methods**  

  - **2.2.1. CNN as feature**

    - Convolutional channel features, ICCV 2015. [[Paper]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_Convolutional_Channel_Features_ICCV_2015_paper.pdf)

    - Learning complexity-aware cascades for deep pedestrian detection, ICCV 2015. [[Paper]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Cai_Learning_Complexity-Aware_Cascades_ICCV_2015_paper.pdf)  

    - Is faster r-cnn doing well for pedestrian detection? ECCV 2016. [[Paper]](https://arxiv.org/pdf/1607.07032.pdf)

    - Learning multilayer channel features for pedestrian detection, TIP 2017. [[Paper]](https://arxiv.org/pdf/1603.00124.pdf)

    - Neural features for pedestrian detection, Neurocomputing 2017. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231217302710)

    - Filtered shallow-deep feature channels for pedestrian detection, Neurocomputing 2017. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231217304897)

    - Pushing the limits of deep cnns for pedestrian detection, TCSVT 2018. [[Paper]](https://arxiv.org/pdf/1603.04525.pdf)

    - Fast pedestrian detection with attention-enhanced multi-scale rpn and soft-cascaded decision trees, TITS 2019. [[paper]](https://ieeexplore.ieee.org/document/8883216/)

    - Hybrid channel based pedestrian detection, Neurocomputing 2020. [[Paper]](https://arxiv.org/pdf/1912.12431.pdf)   

  - **CNN as classifier**

    - Joint deep learning for pedestrian detection, ICCV 2013. [[Paper]](https://ieeexplore.ieee.org/document/6751366)

    - Switchable deep network for pedestrian detection, CVPR 2014. [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Luo_Switchable_Deep_Network_2014_CVPR_paper.pdf)

    - Taking a deeper look at pedestrians, CVPR 2015. [[Paper]](https://arxiv.org/pdf/1501.05790.pdf)

    - Pedestrian detection aided by deep learning semantic tasks, CVPR 2015. [[Paper]](https://www.ee.cuhk.edu.hk/~xgwang/papers/tianLWTcvpr15.pdf)

    - Real-time pedestrian detection with deep network cascades, BMVC 2015. [[Paper]](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43850.pdf)

    - Deep learning strong parts for pedestrian detection, ICCV 2015. [[Paper]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tian_Deep_Learning_Strong_ICCV_2015_paper.pdf)

    - Deep network aided by guiding network for pedestrian detection, PRL 2017. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0167865517300545)

    - Improving the performance of pedestrian detectors using convolutional learning, PR 2017. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S003132031630111X)

    - Scale-aware fast r-cnn for pedestrian detection, TMM 2018. [[Paper]](https://arxiv.org/pdf/1510.08160.pdf)

    - Deepid-net: Object detection with deformable part based convolutional neural networks, TPAMI 2017. [[Paper]](https://wang-zhe.me/welcome_files/papers/ouyangZWpami16.pdf)

    - S-cnn: Subcategory-aware convolutional networks for object detection, TPAMI 2018. [[Paper]](https://ieeexplore.ieee.org/document/8051100)

    - Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection, TPAMI 2018. [[Paper]](https://wlouyang.github.io/Papers/Ouyang2017JoingCNNPed.pdf)

- **2.2.2. Pure CNN based methods**  

  - **Scale-aware methods**

    - Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers, CVPR 2016. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf)

    - A unified multi-scale deep convolutional neural network for fast object detection, ECCV 2016. [[Paper]](https://arxiv.org/pdf/1607.07155.pdf)

    - Scale-adaptive deconvolutional regression network for pedestrian detection, ACCV 2016. [[Paper]](https://link.springer.com/chapter/10.1007/978-3-319-54184-6_26)

    - Sam-rcnn: Scaleaware multi-resolution multi-channel pedestrian detection, BMVC 2018. [[Paper]](https://arxiv.org/pdf/1808.02246.pdf)

    - Fpn++: A simple baseline for pedestrian detection, ICME 2019. [[Paper]](https://svip-lab.github.io/paper/icme2019_hujh.pdf)

    - Ratio-and-scale-aware YOLO for pedestrian detection, TIP 2019. [[Paper]](https://ieeexplore.ieee.org/document/9272650)    

  - **Part-based methods**

    - PCN: Part and context information for pedestrian detection with cnns, BMVC 2017. [[Paper]](https://arxiv.org/pdf/1804.04483.pdf)

    - Joint holistic and partial cnn for pedestrian detection, BMVC 2018. [[Paper]](http://www.bmva.org/bmvc/2018/contents/papers/0261.pdf)

    - Occlusion-aware r-cnn: Detecting pedestrians in a crowd, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Shifeng_Zhang_Occlusion-aware_R-CNN_Detecting_ECCV_2018_paper.pdf)

    - Bi-box regression for pedestrian detection and occlusion estimation, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf)

    - Pedjointnet: Joint headshoulder and full body deep network for pedestrian detection, IEEE Access 2019. [[Paper]](https://ieeexplore.ieee.org/iel7/6287639/8600701/08685094.pdf)

    - Double anchor r-cnn for human detection in a crowd, arXiv 2019. [[Paper]](https://arxiv.org/pdf/1909.09998.pdf)

    - Semantic head enhanced pedestrian detection in a crowd, arXiv 2019. [[Paper]](https://arxiv.org/pdf/1911.11985.pdf)

    - Semantic part rcnn for real-world pedestrian detection, CVPRW 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2019/papers/Weakly%20Supervised%20Learning%20for%20Real-World%20Computer%20Vision%20Applications/Xu_Semantic_Part_RCNN_for_Real-World_Pedestrian_Detection_CVPRW_2019_paper.pdf)

    - Mask-guided attention network for occluded pedestrian detection, ICCV 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Pang_Mask-Guided_Attention_Network_for_Occluded_Pedestrian_Detection_ICCV_2019_paper.pdf)

    - Learning Hierarchical Graph for Occluded Pedestrian Detection, ACM-MM 2020. [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394171.3413983)

    - A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection, TITS 2021. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8960286)

    - Sequential Attention-Based Distinct Part Modeling for Balanced Pedestrian Detection, TITS 2022. [[Paper]](https://ieeexplore.ieee.org/document/9694517)

  - **Attention-based methods**

    - Illuminating pedestrians via simultaneous detection and segmentation, ICCV 2017. [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Brazil_Illuminating_Pedestrians_via_ICCV_2017_paper.pdf)

    - Vis-hud: Using visual saliency to improve human detection with convolutional neural networks, CVPRW 2018. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w39/Gajjar_ViS-HuD_Using_Visual_CVPR_2018_paper.pdf)

    - Graininess-aware deep feature learning for pedestrian detection, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.pdf)

    - Occluded pedestrian detection through guided attention in cnns, CVPR 2018. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Occluded_Pedestrian_Detection_CVPR_2018_paper.pdf)

    - Deep feature fusion by competitive attention for pedestrian detection, IEEE Access 2019. [[Paper]](https://ieeexplore.ieee.org/iel7/6287639/6514899/08629899.pdf)

    - Part-level convolutional neural networks for pedestrian detection using saliency and boundary box alignment, IEEE Access 2019. [[Paper]](https://ieeexplore.ieee.org/iel7/6287639/6514899/08641120.pdf)

    - Multi-grained deep feature learning for robust pedestrian detection, TCSVT 2019. [[Paper]](http://ivg.au.tsinghua.edu.cn/people/Chunze_Lin/TCSVT18_Multi-grained%20Deep%20Feature%20Learning%20for%20Robust%20Pedestrian%20Detection.pdf)

    - Attention guided neural network models for occluded pedestrian detection, PR 2020. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0167865519303733)

  - **Feature-fused methods**

    - Direct multi-scale dual-stream network for pedestrian detection, ICIP 2017. [[Paper]](https://ieeexplore.ieee.org/document/8296262)

    - Accurate single stage detector using recurrent rolling convolution, CVPR 2017. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ren_Accurate_Single_Stage_CVPR_2017_paper.pdf)

    - Object detection based on multilayer convolution feature fusion and online hard example mining, IEEE Access 2018. [[Paper]](https://ieeexplore.ieee.org/iel7/6287639/8274985/08314823.pdf)    

    - Pedestrian detection via body part semantic and contextual information with dnn, TMM 2018. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8345752/)

    - Deep aggregation learning for high-performance small pedestrian detection, ACML 2018. [[Paper]](http://proceedings.mlr.press/v95/shang18a/shang18a.pdf)

    - Learning pixel-level and instance-level context-aware features for pedestrian detection in crowds, IEEE Access 2019. [[Paper]](https://ieeexplore.ieee.org/iel7/6287639/8600701/08763938.pdf)

    - Mfr-cnn: Incorporating multi-scale features and global information for traffic object detection, TVT 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8371213/)

    - Taking a look at small-scale pedestrians and occluded pedestrians, TIP 2020. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8931263/)

    - Coupled network for robust pedestrian detection with gated multi-layer feature extraction and deformable occlusion handling, TIP2021. [[Paper]](https://arxiv.org/pdf/1912.08661.pdf)

    - Object detection with location-aware deformable convolution and backward attention filtering, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Object_Detection_With_Location-Aware_Deformable_Convolution_and_Backward_Attention_Filtering_CVPR_2019_paper.pdf)

    - Temporal-context enhanced detection of heavily occluded pedestrians, CVPR 2020. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wu_Temporal-Context_Enhanced_Detection_of_Heavily_Occluded_Pedestrians_CVPR_2020_paper.pdf)

    - Ground plane context aggregation network for day-and-night on vehicular pedestrian detection, TITS 2020. [[Paper]](https://ieeexplore.ieee.org/document/9091903)    

    - Vehicle and Pedestrian Detection Algorithm Based on Lightweight YOLOv3-Promote and Semi-Precision Acceleration, TITS 2022. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9669164)

    - High quality proposal feature generation for crowded pedestrian detection, PR 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0031320322000863)

    - Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes, IF 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S1566253523000544)

    - Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving, CVPR 2023. [[Paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.pdf)

  - **Cascade-based methods**

    - Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection, WACV 2017. [[Paper]](https://arxiv.org/pdf/1610.03466.pdf)

    - Learning efficient single-stage pedestrian detectors by asymptotic localization fitting, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Wei_Liu_Learning_Efficient_Single-stage_ECCV_2018_paper.pdf)

    - Circlenet: Reciprocating feature adaptation for robust pedestrian detection, TITS 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8848847/)

    - Pedestrian detection with autoregressive network phases, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Brazil_Pedestrian_Detection_With_Autoregressive_Network_Phases_CVPR_2019_paper.pdf)

    - Pedestrian detection: The elephant in the room, arXiv 2020. [[Paper]](https://arxiv.org/pdf/2003.08799.pdf)

    - A one-and-half stage pedestrian detector, WACV 2020. [[Paper]](https://openaccess.thecvf.com/content_WACV_2020/papers/Ujjwal_A_one-and-half_stage_pedestrian_detector_WACV_2020_paper.pdf)

    - Progressive Refinement Network for Occluded Pedestrian Detection, ECCV 2020. [[Paper]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680035.pdf)

    - SADet: Learning An Efficient and Accurate Pedestrian Detector, IJCB 2021. [[Paper]](https://ieeexplore.ieee.org/document/9484371)

    - F2DNet: Fast Focal Detection Network for Pedestrian Detection, ICPR 2022. [[Paper]](https://arxiv.org/abs/2203.02331)

  - **Anchor-free methods**

    - Small-scale pedestrian detection based on topological line localization and temporal feature aggregation, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Tao_Song_Small-scale_Pedestrian_Detection_ECCV_2018_paper.pdf)

    - High-level semantic feature detection: A new perspective for pedestrian detection, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_High-Level_Semantic_Feature_Detection_A_New_Perspective_for_Pedestrian_Detection_CVPR_2019_paper.pdf)

    - Attribute-aware pedestrian detection in a crowd, TMM 2021. [[Paper]](https://arxiv.org/pdf/1910.09188.pdf)

    - OAF-Net: An Occlusion-Aware Anchor-Free Network for Pedestrian Detection in a Crowd, TITS 2022. [[Paper]](https://ieeexplore.ieee.org/document/9770474)

    - Effectiveness of Vision Transformer for Fast and Accurate Single-Stage Pedestrian Detection, NeurIPS 2022. [[Paper]](https://openreview.net/forum?id=eow_ZGaw24j)

    - Cascade Transformer Decoder based Occluded Pedestrian Detection with Dynamic Deformable Convolution and Gaussian Projection Channel Attention Mechanism, TMM 2023. [[Paper]](https://ieeexplore.ieee.org/abstract/document/10057083)

    - Pedestrian Detection Using MB-CSP Model and Boosted Identity Aware Non-Maximum Suppression, TITS 2022. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9857630)

    - Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection, CVPR 2023. [[Paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.pdf)

    - CFRLA-Net: A Context-aware Feature Representation Learning Anchor-free Network for Pedestrian Detection, TCSVT2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10045738)

    

  - **Data-augmentation methods**

    - Synthesizing a scene-specific pedestrian detector and pose estimator for static video surveillance, IJCV 2018. [[Paper]](http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf)

    - Training cascade compact cnn with region-iou for accurate pedestrian detection, TITS 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8796403/)

    - A shape transformation-based dataset augmentation framework for pedestrian detection, arXiv 2019. [[Paper]](https://arxiv.org/pdf/1912.07010.pdf)

    - Advanced pedestrian dataset augmentation for autonomous driving, ICCVW 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCVW_2019/papers/ADW/Vobecky_Advanced_Pedestrian_Dataset_Augmentation_for_Autonomous_Driving_ICCVW_2019_paper.pdf)

    - Pmc-gans: Generating multi-scale high-quality pedestrian with multimodal cascaded gans, BMVC 2019. [[Paper]](https://arxiv.org/pdf/1912.12799.pdf)

    - Pedhunter: Occlusion robust pedestrian detector in crowded scenes, AAAI 2020. [[Paper]](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-ChiC.961.pdf)

    - Where, what, whether: Multi-modal learning meets pedestrian detection, CVPR 2020. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Luo_Where_What_Whether_Multi-Modal_Learning_Meets_Pedestrian_Detection_CVPR_2020_paper.pdf)

    - Low-illumination image enhancement for night-time uav pedestrian detection, TII 2020. [[Paper]](https://ieeexplore.ieee.org/document/9204832)

    - AutoPedestrian: An Automatic Data Augmentation and Loss Function Search Scheme for Pedestrian Detection, TIP 20201. [[Paper]](https://ieeexplore.ieee.org/document/9563123)

    - Impartial Differentiable Automatic Data Augmentation Based on Finite Difference Approximation for Pedestrian Detection, TIM 2022. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9781425)

    - Pedestrian Detection Using Multi-Scale Structure-Enhanced Super-Resolution, IEEE TITS2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10168965)

  - **Loss-driven methods**

    - Perceptual generative adversarial networks for small object detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.pdf)

    - Mimicking very efficient network for object detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf)

    - Fused discriminative metric learning for low resolution pedestrian detection, ICIP 2017. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8451791/)

    - Boosted convolutional neural networks (bcnn) for pedestrian detection, WACV 2017. [[Paper]](https://ieeexplore.ieee.org/abstract/document/7926649/)

    - Subcategory-aware convolutional neural networks for object proposals and detection, WACV 2017. [[Paper]](https://arxiv.org/pdf/1604.04693.pdf)

    - Repulsion loss: Detecting pedestrians in a crowd, CVPR 2018. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Repulsion_Loss_Detecting_CVPR_2018_paper.pdf)

    - Learning lightweight pedestrian detector with hierarchical knowledge distillation, ICIP 2019. [[Paper]](https://arxiv.org/pdf/1909.09325.pdf)

    - Discriminative feature transformation for occluded pedestrian detection, ICCV 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Discriminative_Feature_Transformation_for_Occluded_Pedestrian_Detection_ICCV_2019_paper.pdf)

    - Count- and Similarity-aware R-CNN for Pedestrian Detection, ECCV 2020. [[Paper]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620086.pdf)

    - Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection, ArXiv 2021. [[Paper]](https://arxiv.org/pdf/2103.10091.pdf)

    - LLA: Loss-aware Label Assignment for Dense Pedestrian Detection, ArXiv 2021. [[Paper]](https://arxiv.org/pdf/2101.04307.pdf)

    - Pedestrian Detection by Exemplar-Guided Contrastive Learning, ArXiv 20201. [[Paper]](https://arxiv.org/abs/2111.08974)

    - Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning, TITS 2021. [[Paper]](https://ieeexplore.ieee.org/document/9507387)

    - VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision, CVPR 2023. [[Paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.pdf)

  - **Post-processing methods**

    - End-to-end people detection in crowded scenes, CVPR 2016. [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Stewart_End-To-End_People_Detection_CVPR_2016_paper.pdf)

    - Led: Localization-quality estimation embedded detector, ICIP 2018. [[Paper]](http://resources.dbgns.com/study/ObjectDetection/NMS-LED.pdf)

    - Learning to separate: Detecting heavily-occluded objects in urban scenes, arXiv 2019. [[Paper]](https://arxiv.org/pdf/1912.01674.pdf)

    - Single shot multibox detector with kalman filter for online pedestrian detection in video, IEEE Access 2019. [[Paper]](https://ieeexplore.ieee.org/iel7/6287639/6514899/08631151.pdf)

    - Adaptive nms: Refining pedestrian detection in a crowd, CVPR 2019. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Adaptive_NMS_Refining_Pedestrian_Detection_in_a_Crowd_CVPR_2019_paper.pdf)

    - S3d: Scalable pedestrian detection via score scale surface discrimination, TCSVT 2020. [[Paper]](https://www.researchgate.net/profile/Xiao_Wang336/publication/332650146_S3D_Scalable_Pedestrian_Detection_via_Score_Scale_Surface_Discrimination/links/5e1e8b6a299bf136303ac9b9/S3D-Scalable-Pedestrian-Detection-via-Score-Scale-Surface-Discrimination.pdf)

    - Nms by representative region: Towards crowded pedestrian detection by proposal pairing, CVPR 2020. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_NMS_by_Representative_Region_Towards_Crowded_Pedestrian_Detection_by_Proposal_CVPR_2020_paper.pdf)

    - NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination, ACM-MM 2020. [[Paper]](https://arxiv.org/pdf/2007.13376.pdf)

    - DETR for Pedestrian Detection, Arxiv 2020. [[Paper]](https://arxiv.org/abs/2012.06785)

    - NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection, ICMR 2021. [[Paper]](https://arxiv.org/abs/2106.02426)

    - Pedestrian Detection for Autonomous Cars: Inference Fusion of Deep Neural Networks, IEEE TITS 2022. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9906846)

    - Region NMS-based deep network for gigapixel level pedestrian detection with two-step cropping, Neurocomputing 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0925231221014843)

    - OTP-NMS: Toward Optimal Threshold Prediction of NMS for Crowded Pedestrian Detection, IEEE TIP2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10130101)

    - Neural Attention-Driven Non-Maximum Suppression for Person Detection, TIP2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10107719)

  - **Multi-task methods**

    - What can help pedestrian detection? CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf)

    - Accurate pedestrian detection by human pose regression, TIP 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8850309/)

    - Human detection aided by deeply learned semantic masks, TCSVT 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8746171/)

    - Cluenet: A deep framework for occluded pedestrian pose estimation, BMVC 2019. [[Paper]](https://www.researchgate.net/profile/Sudip_Das12/publication/337831088_ClueNet_A_Deep_Framework_for_Occluded_Pedestrian_Pose_Estimation/links/5dee00bf4585159aa46e8d05/ClueNet-A-Deep-Framework-for-Occluded-Pedestrian-Pose-Estimation.pdf)

    - Semantic part rcnn for real-world pedestrian detection, CVPRW 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2019/papers/Weakly%20Supervised%20Learning%20for%20Real-World%20Computer%20Vision%20Applications/Xu_Semantic_Part_RCNN_for_Real-World_Pedestrian_Detection_CVPRW_2019_paper.pdf)

    - Re-id driven localization refinement for person search, ICCV 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Han_Re-ID_Driven_Localization_Refinement_for_Person_Search_ICCV_2019_paper.pdf)

    - PEN: Pose-embedding network for pedestrian detection, TCSVT 2020. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9109304) 

    - A unified multi-task learning architecture for fast and accurate pedestrian detection, TITS 2020. [[Paper]](https://ieeexplore.ieee.org/document/9186837)

    - Enhanced Multi-Task Learning Architecture for Detecting Pedestrian at Far Distance, TITS 2022. [[Paper]](https://ieeexplore.ieee.org/document/9690771)

    - Detachable Crowd Density Estimation Assisted Pedestrian Detection, TITS 2023. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9963778)

    - Urban scene based Semantical Modulation for Pedestrian Detection, Neurocomputing 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0925231221018026)

  - **Generalization**

    - Generalizable Pedestrian Detection: The Elephant In The Room, CVPR 2021. [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Hasan_Generalizable_Pedestrian_Detection_The_Elephant_in_the_Room_CVPR_2021_paper.pdf) 

    - Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond, arXiv 2022. [[Paper]](https://arxiv.org/abs/2201.03176) 

    - Source-Free Unsupervised Cross-Domain Pedestrian Detection via Pseudo Label Mining and Screening, ICME 2022. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9859707)

  - **others**

    - Exploiting target data to learn deep convolutional networks for scene-adapted human detection, TIP 2018. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8125769/)

    - Deep learning approaches on pedestrian detection in hazy weather, TIE 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8880634/)

    - Pedestrian detection from thermal images using saliency maps, CVPRW 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2019/papers/PBVS/Ghose_Pedestrian_Detection_in_Thermal_Images_Using_Saliency_Maps_CVPRW_2019_paper.pdf)

    - Domainadaptive pedestrian detection in thermal images, ICIP 2019. [[Paper]](https://assets.amazon.science/26/2c/85e236d84454b06bc972684227ce/domain-adaptive-pedestrian-detection-in-thermal-images.pdf)

    - Spatial focal loss for pedestrian detection in fisheye imagery, WACV 2019.[[Paper]](https://ieeexplore.ieee.org/abstract/document/8658951/)

    - Oriented spatial transformer network for pedestrian detection using fish-eye camera, TMM 2020. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8772168/)

    - Semi-supervised human detection via region proposal networks aided by verification, TIP 2020. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8858040/)

    - Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery, ECCV 2020. [[Paper]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670545.pdf)

    - Self-bootstrapping pedestrian detection in downward-viewing fisheye cameras using pseudo-labeling, ICME 2020. [[Paper]](https://ieeexplore.ieee.org/document/9102923)

    - Joint pedestrian detection and risk-level prediction with motion-representation-by-detection, ICRA 2020. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9197399)

    - Adversarial training-based hard example mining for pedestrian detection in fish-eye images, TITS 2020. [[Paper]](https://ieeexplore.ieee.org/document/9145843)

    - Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection, CVIU 2021.  [[Paper]](https://arxiv.org/abs/2104.13764)

    - Unreliable-to-Reliable Instance Translation for Semi-Supervised Pedestrian Detection, TMM 2021. [[Paper]](https://ieeexplore.ieee.org/document/9352521)

    - Real-time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic, TMM 2021. [[Paper]](https://ieeexplore.ieee.org/document/9417704)

    - Radio-Assisted Human Detection, TMM 2022. [[Paper]](https://arxiv.org/abs/2112.08743)

    - STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes, CVPR 2022. [[Paper]](https://arxiv.org/abs/2204.01026)

    - Accurate and Real-Time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network, IEEE RAL 2023. [[Paper]](https://ieeexplore.ieee.org/abstract/document/10003992)

    - PEDRo: an Event-based Dataset for Person Detection in Robotics, CVPRW2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10208992)

    - NIRPed: A Novel Benchmark for Nighttime Pedestrian and Its Distance Joint Detection, TITS2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10077447)

## 3. Multispectral pedestrian detection 

#### 3.1. Deep feature fusion 

  - Multispectral deep neural networks for pedestrian detection, BMVC 2016. [[Paper]](https://arxiv.org/pdf/1611.02644.pdf)

  - Fully convolutional region proposal networks for multispectral person detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017_workshops/w3/papers/Konig_Fully_Convolutional_Region_CVPR_2017_paper.pdf)

  - Pedestrian detection for autonomous vehicle using multi-spectral cameras, TIV 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8671738/)

  - Fusion of multispectral data through illuminationaware deep neural networks for pedestrian detection, IF 2019. [[Paper]](https://arxiv.org/pdf/1802.09972.pdf)

  - Illuminationaware faster r-cnn for robust multispectral pedestrian detection, PR 2019. [[Paper]](https://arxiv.org/pdf/1803.05347.pdf)

  - Cross-modality interactive attention network for multispectral pedestrian detection, IF 2019. [[Paper]](https://www.researchgate.net/profile/Shifeng_Zhang4/publication/327885485_Cross-Modality_Interactive_Attention_Network_for_Multispectral_Pedestrian_Detection/links/5c718c8f458515831f699042/Cross-Modality-Interactive-Attention-Network-for-Multispectral-Pedestrian-Detection.pdf)

  - Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems, ECCV 2020. [[Paper]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630766.pdf)

  - Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving, arXiv 2021. [[Paper]](https://arxiv.org/abs/2105.12713)

  - Guided Attentive Feature Fusion for Multispectral Pedestrian Detection, WACV 2021. [[Paper]](https://openaccess.thecvf.com/content/WACV2021/papers/Zhang_Guided_Attentive_Feature_Fusion_for_Multispectral_Pedestrian_Detection_WACV_2021_paper.pdf)

  - Uncertainty-Guided Cross-Modal Learning for Robust Multispectral Pedestrian Detection, TCSVT 2021. [[Paper]](https://ieeexplore.ieee.org/document/9419080)

  - Deep Cross-Modal Representation Learning and Distillation for Illumination-Invariant Pedestrian Detection, TCSVT 2022. [[Paper]](https://ieeexplore.ieee.org/document/9357413)

  - Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving, TITS 2021. [[Paper]](https://arxiv.org/pdf/2105.12713.pdf)

  - BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection, ArXiv 2021. [[Paper]](https://arxiv.org/abs/2112.02277)

  - Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian Detection, TMM 2022. [[Paper]](https://ieeexplore.ieee.org/document/9739079)

  - Multispectral interaction convolutional neural network for pedestrian detection, CVIU 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S1077314222001321)

  - Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection, Information Fusion 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S1566253522000549)

  - Learning a Dynamic Cross-Modal Network for Multispectral Pedestrian Detection, ACMMM 2022. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3503161.3547895)

  - Multiscale Cross-modal Homogeneity Enhancement and Confidence-aware Fusion for Multispectral Pedestrian Detection, TMM 2023. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10114594)

#### 3.2. Data processing 

  - Multispectral pedestrian detection via simultaneous detection and segmentation, BMVC 2018. [[Paper]](https://arxiv.org/pdf/1808.04818.pdf)

  - Weakly aligned cross-modal learning for multispectral pedestrian detection, ICCV 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Weakly_Aligned_Cross-Modal_Learning_for_Multispectral_Pedestrian_Detection_ICCV_2019_paper.pdf)

  - Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU, MVA 2021. [[Paper]](https://arxiv.org/pdf/2107.11196.pdf)

#### 3.3. Domain adaptation 

  - Learning cross-modal deep representations for robust pedestrian detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Xu_Learning_Cross-Modal_Deep_CVPR_2017_paper.pdf)

  - Unsupervised domain adaptation for multispectral pedestrian detection, CVPRW 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2019/papers/MULA/Guan_Unsupervised_Domain_Adaptation_for_Multispectral_Pedestrian_Detection_CVPRW_2019_paper.pdf)

  - Pedestrian detection with unsupervised multispectral feature learning using deep neural networks, IF 2019. [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S1566253517305948)

  - Unsupervised thermal-to-visible domain adaptation method for pedestrian detection, PRL 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0167865521004153)

## 4. Datasets 

#### 4.1. Earlier pedestrian datasets 

   - A trainable system for object detection, IJCV 2000. [[Paper]](https://link.springer.com/article/10.1023/A:1008162616689)

   - Histograms of oriented gradients for human detection, CVPR 2005. [[Paper]](https://hal.inria.fr/docs/00/54/85/12/PDF/hog_cvpr2005.pdf)

   - Depth and appearance for mobile scene analysis, ICCV 2007. [[Paper]](https://homes.esat.kuleuven.be/~konijn/publications/2007/eth_biwi_00498.pdf)

   - Multi-cue onboard pedestrian detection, CVPR 2009. [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.716.9022&rep=rep1&type=pdf)

   - Monocular pedestrian detection: Survey and experiments, TPAMI 2009. [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.599&rep=rep1&type=pdf

)

#### 4.2. Modern pedestrian datasets 

   - Pedestrian detection: An evaluation of the state of the art, PAMI 2010. [[Paper]](https://wiki.epfl.ch/edicpublic/documents/Candidacy%20exam/01Ped.pdf)

   - Are we ready for autonomous driving? the kitti vision benchmark suite, CVPR 2012. [[Paper]](http://www.webmail.cvlibs.net/publications/Geiger2012CVPR.pdf)

   - Citypersons: A diverse dataset for pedestrian detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_CityPersons_A_Diverse_CVPR_2017_paper.pdf)

   - Nightowls: A pedestrians at night dataset, ACCV 2018. [[Paper]](https://ora.ox.ac.uk/objects/uuid:48f374c8-eac3-4a98-8628-92039a76c17b/download_file?file_format=pdf&safe_filename=neumann18b.pdf&type_of_work=Conference+item)

   - Crowdhuman: A benchmark for detecting human in a crowd, arXiv 2018. [[Paper]](https://arxiv.org/pdf/1805.00123.pdf)

   - Eurocity persons: A novel benchmark for person detection in traffic scenes, PAMI 2019. [[Paper]](https://www.researchgate.net/profile/Dariu_Gavrila/publication/330891380_EuroCity_Persons_A_Novel_Benchmark_for_Person_Detection_in_Traffic_Scenes/links/5d395fdb299bf1995b487c21/EuroCity-Persons-A-Novel-Benchmark-for-Person-Detection-in-Traffic-Scenes.pdf)

   - Widerperson: A diverse dataset for dense pedestrian detection in the wild, TMM 2020. [[Paper]](https://arxiv.org/pdf/1909.12118.pdf)

   - TJU-DHD: A Diverse High-Resolution Dataset for Object Detection, TIP 2020. [[Paper]](https://arxiv.org/pdf/2011.09170.pdf)

   - MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking? ICCV 2021. [[Paper]](https://arxiv.org/pdf/2108.09518.pdf)

     

#### 4.3. Multispectral pedestrian datasets 

   - Multispectral pedestrian detection: Benchmark dataset and baseline, CVPR 2015. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2015/papers/Hwang_Multispectral_Pedestrian_Detection_2015_CVPR_paper.pdf)

   - Pedestrian detection at day/night time with visible and fir cameras: A comparison, PR 2016. [[Paper]](https://www.mdpi.com/1424-8220/16/6/820)

   - LLVIP: A Visible-infrared Paired Dataset for Low-light Vision, ICCV workshop 2021. [[Paper]](https://arxiv.org/pdf/2108.10831.pdf)

## 5. Challenges 

#### 5.1. Scale variance 

   - A unified multi-scale deep convolutional neural network for fast object detection, ECCV 2016. [[Paper]](https://arxiv.org/pdf/1607.07155.pdf)

   - Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers, CVPR 2016. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf)

   - Feature pyramid networks for object detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.pdf)

   - Perceptual generative adversarial networks for small object detection, CVPR 2017. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.pdf)

   - Task-driven super resolution: Object detection in low-resolution images, arXiv 2018. [[Paper]](https://arxiv.org/pdf/1803.11316.pdf)

   - High-level semantic networks for multi-scale object detection, TCSVT 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8887288/)

   - Small-scale pedestrian detection based on deep neural network, TITS 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8751139/)

   - Scale-aware fast r-cnn for pedestrian detection, TMM 2019. [[Paper]](https://arxiv.org/pdf/1510.08160.pdf)

   - Jcs-net: Joint classification and super-resolution network for small-scale pedestrian detection in surveillance images, TIFS 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8714071/)

   - Multi-resolution generative adversarial networks for tinyscale pedestrian detection, ICIP 2019. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8803030/)

   - Taking a look at small-scale pedestrians and occluded pedestrians, TIP 2020. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8931263/)

   - Scale match for tiny person detection, WACV 2020. [[Paper]](https://openaccess.thecvf.com/content_WACV_2020/papers/Yu_Scale_Match_for_Tiny_Person_Detection_WACV_2020_paper.pdf)

   - Self-Mimic Learning for Small-scale Pedestrian Detection, ACM-MM 2020. [[Paper]](https://cse.buffalo.edu/~jsyuan/papers/2020/SML.pdf)

   - Box Guided Convolution for Pedestrian Detection, ACM-MM 2020. [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394171.3413989)

   - Effective Fusion Factor in FPN for Tiny Object Detection, WACV 2021. [[Paper]](https://arxiv.org/pdf/2011.02298.pdf)

   - KGSNet: Key-point-guided super-resolution network for pedestrian detection in the wild, TNNLS 2020. [[Paper]](https://ieeexplore.ieee.org/document/9137724/)   

   - Robust Small-scale Pedestrian Detection with Cued Recall via Memory Learning, ICCV 2021. [[Paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Robust_Small-Scale_Pedestrian_Detection_With_Cued_Recall_via_Memory_Learning_ICCV_2021_paper.pdf)  

   - Pedestrian Detection Using Stationary Wavelet Dilated Residual Super-Resolution, TIM 2022. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9678120)

#### 5.2. Occlusion 

   - An hog-lbp human detector with partial occlusion handling, ICCV 2010. [[Paper]](https://www.researchgate.net/profile/Tony_Han3/publication/224135946_An_HOG-LBP_human_detector_with_partial_occlusion_handling/links/0046351affdef73b37000000.pdf)

   - Handling occlusions with franken-classifiers, CVPR 2013. [[Paper]](https://openaccess.thecvf.com/content_iccv_2013/papers/Mathias_Handling_Occlusions_with_2013_ICCV_paper.pdf)

   - Deep learning strong parts for pedestrian detection, ICCV 2015. [[Paper]](https://openaccess.thecvf.com/content_iccv_2015/papers/Tian_Deep_Learning_Strong_ICCV_2015_paper.pdf)

   - Multi-label learning of part detectors for heavily occluded pedestrian detection, ICCV 2017. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Multi-Label_Learning_of_ICCV_2017_paper.pdf)

   - Repulsion loss: Detecting pedestrians in a crowd, CVPR 2018. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Repulsion_Loss_Detecting_CVPR_2018_paper.pdf)

   - Improving occlusion and hard negative handling for single-stage pedestrian detectors, CVPR 2018. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Noh_Improving_Occlusion_and_CVPR_2018_paper.pdf)

   - Bi-box regression for pedestrian detection and occlusion estimation, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf)

   - Occlusion-aware r-cnn: Detecting pedestrians in a crowd, ECCV 2018. [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Shifeng_Zhang_Occlusion-aware_R-CNN_Detecting_ECCV_2018_paper.pdf)

   - Adaptive nms: Refining pedestrian detection in a crowd, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Adaptive_NMS_Refining_Pedestrian_Detection_in_a_Crowd_CVPR_2019_paper.pdf)

   - Mask-guided attention network for occluded pedestrian detection, ICCV 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Pang_Mask-Guided_Attention_Network_for_Occluded_Pedestrian_Detection_ICCV_2019_paper.pdf)

   - Learning to separate: Detecting heavily-occluded objects in urban scenes, arXiv 2019. [[Paper]](https://arxiv.org/pdf/1912.01674.pdf)

   - Taking a look at small-scale pedestrians and occluded pedestrians, TIP 2020. [[Paper]](https://ieeexplore.ieee.org/abstract/document/8931263/)   

   - Psc-net: Learning part spatial cooccurence for occluded pedestrian detection, arXiv 2020. [[Paper]](https://arxiv.org/pdf/2001.09252.pdf)

   - Detection in crowded scenes: One proposal, multiple predictions, CVPR 2020. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chu_Detection_in_Crowded_Scenes_One_Proposal_Multiple_Predictions_CVPR_2020_paper.pdf)

   - Pedhunter: Occlusion robust pedestrian detector in crowded scenes, AAAI 2020. [[Paper]](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-ChiC.961.pdf)

   - Relational learning for joint head and human detection, AAAI 2020. [[Paper]](https://arxiv.org/pdf/1909.10674.pdf)

   - V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection, ArXiv 2021. [[Paper]](https://arxiv.org/pdf/2104.03106.pdf)

   - Variational Pedestrian Detection, arXiv 2021. [[Paper]](https://arxiv.org/abs/2104.12389)

   - Detachable Crowd Density Estimation Assisted Pedestrian Detection, TITS 2023. [[Paper]](https://ieeexplore.ieee.org/abstract/document/9963778)

   - Cascade Transformer Decoder based Occluded Pedestrian Detection with Dynamic Deformable Convolution and Gaussian Projection Channel Attention Mechanism, TMM 2023. [[Paper]](https://ieeexplore.ieee.org/abstract/document/10057083)

   - High quality proposal feature generation for crowded pedestrian detection, PR 2022. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0031320322000863)

   - The Impact of Partial Occlusion on Pedestrian Detectability, arXiv 2022. [[Paper]](https://arxiv.org/pdf/2205.04812.pdf)

   - DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection, arXiv 2023. [[Paper]](https://arxiv.org/pdf/2301.05565.pdf)

#### 5.3. Domain adaptation 

   - Domain adaptive faster r-cnn for object detection in the wild, CVPR 2018. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Domain_Adaptive_Faster_CVPR_2018_paper.pdf)

   - Progressive domain adaptation for object detection, CVPRW 2018. [[Paper]](https://openaccess.thecvf.com/content_WACV_2020/papers/Hsu_Progressive_Domain_Adaptation_for_Object_Detection_WACV_2020_paper.pdf)

   - A robust learning approach to domain adaptive object detection, ICCV 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Khodabandeh_A_Robust_Learning_Approach_to_Domain_Adaptive_Object_Detection_ICCV_2019_paper.pdf)

   - Diversify and match: A domain adaptive representation learning paradigm for object detection, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Diversify_and_Match_A_Domain_Adaptive_Representation_Learning_Paradigm_for_CVPR_2019_paper.pdf)

   - Domain adaptation for object detection via style consistency, BMVC 2019. [[Paper]](https://arxiv.org/pdf/1911.10033.pdf)

   - Strong-weak distribution alignment for adaptive object detection, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Saito_Strong-Weak_Distribution_Alignment_for_Adaptive_Object_Detection_CVPR_2019_paper.pdf)

   - Few-shot adaptive faster r-cnn, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Few-Shot_Adaptive_Faster_R-CNN_CVPR_2019_paper.pdf)

   - Multi-level domain adaptive learning for cross-domain detection, ICCVW 2019. [[Paper]](https://openaccess.thecvf.com/content_ICCVW_2019/papers/TASK-CV/Xie_Multi-Level_Domain_Adaptive_Learning_for_Cross-Domain_Detection_ICCVW_2019_paper.pdf)

   - Adapting object detectors via selective cross-domain alignment, CVPR 2019. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Adapting_Object_Detectors_via_Selective_Cross-Domain_Alignment_CVPR_2019_paper.pdf)

   - Conditional weighted ensemble of transferred models for camera based onboard pedestrian detection in railway driver support systems, TVT 2020. [[Paper]](https://ieeexplore.ieee.org/document/9050835)

   - Progressive latent models for self-learning scene-specific pedestrian detectors, TITS 2020. [[Paper]](https://ieeexplore.ieee.org/document/8701617)

   - Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection, ArXiv 2021. [[Paper]](https://arxiv.org/pdf/2102.00595.pdf)

   - SAN: Selective Alignment Network for Cross-Domain Pedestrian Detection, TIP 2021. [[Paper]](https://ieeexplore.ieee.org/document/9329171)

## 6. Related survey 

  - Survey of Pedestrian Detection for Advanced Driver Assistance Systems, TPAMI 2009. [[Paper]](https://ieeexplore.ieee.org/document/5010438)

  - Ten Years of Pedestrian Detection, What Have We Learned, ECCV 2014. [[Paper]](https://arxiv.org/pdf/1411.4304.pdf)  

  - Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art, IEEE Access 2019. [[Paper]](https://ieeexplore.ieee.org/document/8684823)

  - Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey, TITS 2021. [[Paper]](https://ieeexplore.ieee.org/document/9440863/)

## 7. Multi-View Pedestrian Detection 

  - Multicamera People Tracking with a Probabilistic Occupancy Map, TPAMI 2018. [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4359319)

  - RCNN & clustering Multi-view people tracking via hierarchical trajectory composition, CVPR 2016. [[Paper]]( https://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_Multi-View_People_Tracking_CVPR_2016_paper.pdf)

  - Deep Multi-Camera People Detection, ICMLA 2017. [[Paper]](https://arxiv.org/pdf/1702.04593.pdf)

  - Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection, ICCV 2017. [[Paper]](https://openaccess.thecvf.com/content_ICCV_2017/papers/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.pdf)

  - Multiview Detection with Feature Perspective Transformation, ECCV 2020. [[Paper]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520001.pdf)

  - Multiview detection with shadow transformer (and view-coherent data augmentation), ACMMM 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3474085.3475310)

  - Voxelized 3D Feature Aggregation for Multiview Detection, arXiv 2021. [[Paper]]( https://arxiv.org/abs/2112.03471)

  - Stacked Homography Transformations for Multi-View Pedestrian Detection, ICCV 2021. [[Paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Song_Stacked_Homography_Transformations_for_Multi-View_Pedestrian_Detection_ICCV_2021_paper.pdf)

  - Multiview Detection with Cardboard Human Modeling, arXiv 2022. [[Paper]]( https://arxiv.org/pdf/2207.02013.pdf)

  - Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention, arXiv 2022. [[Paper]](https://arxiv.org/abs/2208.09211)

  - 3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization, ECCV 2022. [[Paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136700681.pdf)

  - Exploiting key points supervision and grouped feature fusion for multiview pedestrian detection, PR 2022 [[Paper]](https://www.sciencedirect.com/science/article/pii/S0031320322003478)

  - Multi-view Target Transformation for Pedestrian Detection, WACVW 2023. [[Paper]]( https://openaccess.thecvf.com/content/WACV2023W/RWS/papers/Lee_Multi-View_Target_Transformation_for_Pedestrian_Detection_WACVW_2023_paper.pdf)

  - Bringing Generalization to Deep Multi-View Pedestrian Detection, WACVW 2023. [[Paper]](https://openaccess.thecvf.com/content/WACV2023W/RWS/html/Vora_Bringing_Generalization_to_Deep_Multi-View_Pedestrian_Detection_WACVW_2023_paper.html)

  - Two-level Data Augmentation for Calibrated Multi-view Detection, WACV 2023. [[Paper]](https://openaccess.thecvf.com/content/WACV2023/papers/Engilberge_Two-Level_Data_Augmentation_for_Calibrated_Multi-View_Detection_WACV_2023_paper.pdf)

 

## 9. Citation 

If this project help your research, please consider to cite our survey paper.

```

@article{Cao_PDR_TPAMI_2020,

        author = {Jiale Cao and Yanwei Pang and Jin Xie and Fahad Shahbaz Khan and Ling Shao},

        title = {From Handcrafted to Deep Features for Pedestrian Detection: A Survey},

        journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},

        volume = {44},

        number = {9},

        year = {2022},

        pages = {4913-4934},

        }

```

## Contact 

Please contact us if you have any questions.
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