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https://github.com/lcylmhlcy/awesome-occlusion-detection

A survey of occlusion handling in object detection
https://github.com/lcylmhlcy/awesome-occlusion-detection

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A survey of occlusion handling in object detection

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# awesome-occlusion-detection

A survey of occlusion handling in object detection/recognition



### Survey

- 遮挡目标检测与识别技术研究_蔡星艳

- Occlusion Detection and Handling: A Review

- Occlusion Handling in Generic Object Detection: A Review

- Survey of pedestrian detection with occlusion



### General

- [CVPR2006] [The layout consistent random field for recognizing and segmenting partially occluded objects](https://ieeexplore.ieee.org/abstract/document/1640739/)

- [CVPR2011] [A segmentation-aware object detection model with occlusion handling](https://ieeexplore.ieee.org/abstract/document/5995623)

- [CVIU2013] [Occlusion cues for image scene layering](https://www.sciencedirect.com/science/article/abs/pii/S1077314212001300)

- [IV2013] [Occlusion handling using discriminative model of trained part templates and conditional random field](https://ieeexplore.ieee.org/abstract/document/6629557)

- [ICANN2013] [Using the Analytic Feature Framework for the Detection of Occluded Objects](https://link.springer.com/chapter/10.1007/978-3-642-40728-4_75)

- [ICANN2014] [A two-stage classifier architecture for detecting objects under real-world occlusion patterns](https://www.honda-ri.de/pubs/pdf/2693.pdf)

- [BMVC2016] [Measuring the effect of nuisance variables on classifiers](https://infoscience.epfl.ch/record/220613)

- [ECCV2016] [Amodal instance segmentation](https://arxiv.org/abs/1604.08202)

- [2017] [Detecting semantic parts on partially occluded objects](https://arxiv.org/abs/1707.07819)

- [2017] [Improved regularization of convolutional neural networks with cutout](https://arxiv.org/abs/1708.04552)

- [CVPR2017] [A-fast-rcnn: Hard positive generation via adversary for object detection](https://arxiv.org/abs/1704.03414)

- [CVPR2018] [Segan: Segmenting and generating the invisible](https://arxiv.org/abs/1703.10239)

  - 提出DYCE数据集

- [CVPR2018] [Deepvoting: A robust and explainable deep network for semantic part detection under partial occlusion](https://arxiv.org/abs/1709.04577)

- [2019] [Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models](https://arxiv.org/abs/1905.04598)

- [2019] [Tdapnet: Prototype network with recurrent top-down attention for robust object classification under partial occlusion](https://arxiv.org/abs/1909.03879)

- [CVPR2019] [Amodal instance segmentation with kins dataset](https://openreview.net/forum?id=H7AvC4XeO6r)

  - 提出KITTI数据集，针对amodal实例分割任务

- [WACV2019] [Learning to see the invisible: End-to-end trainable amodal instance segmentation](https://arxiv.org/abs/1804.08864)

- [IJCV2020] [Deep Learning for Generic Object Detection: A Survey](https://arxiv.org/abs/1809.02165)

  - 只提到了一点关于occlusion

- [CVPR2020] [Robust object detection under occlusion with context-aware compositionalnets](https://arxiv.org/abs/2005.11643)

  - 提出新遮挡数据集 OccludedCOCO

- [CVPR2020] [Self-supervised scene de-occlusion](https://arxiv.org/abs/2004.02788)

- [IJCV2021] [Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion](https://arxiv.org/pdf/1905.04598.pdf)

  

### Pedestrian Detection

- [ICCV2009] [An HOG-LBP human detector with partial occlusion handling](https://ieeexplore.ieee.org/abstract/document/5459207)

- [CVPR2010] [Multi-cue pedestrian classification with partial occlusion handling](https://ieeexplore.ieee.org/abstract/document/5540111)

- [ICCV2015] [Deep learning strong parts for pedestrian detection](https://openaccess.thecvf.com/content_iccv_2015/html/Tian_Deep_Learning_Strong_ICCV_2015_paper.html)

- [ECCV2018] [Occlusion-aware R-CNN: detecting pedestrians in a crowd](https://arxiv.org/abs/1807.08407)

- [CVPR2018] [Repulsion Loss: Detecting Pedestrians in a Crowd](https://arxiv.org/abs/1711.07752)

- [TITS2019] [Overcoming Occlusion in the Automotive Environment—A Review](https://ieeexplore.ieee.org/abstract/document/8928542/)

- [2021] [Survey of pedestrian detection with occlusion](https://link.springer.com/article/10.1007/s40747-020-00206-8)



### Face detection

- [TPAMI2017] [Faceness-net: Face detection through deep facial part responses](https://arxiv.org/abs/1701.08393)

- [ICMR2020] [Occlusion-Aware GAN for Face De-Occlusion in the Wild](https://ieeexplore.ieee.org/abstract/document/9102788)

- [2020] [A survey of face recognition techniques under occlusion](https://arxiv.org/abs/2006.11366)



### Car Detection

- [TITS2008] [Multilevel Framework to Detect and Handle Vehicle Occlusion](https://ieeexplore.ieee.org/abstract/document/4445682)

- [SP2015] [Inferring occluded features for fast object detection](https://www.sciencedirect.com/science/article/abs/pii/S0165168414004940)

- [CVPR2019] [Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks](https://openreview.net/pdf?id=_cv1Qp4e3x)

- [WACV2020] [Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion](https://arxiv.org/abs/1905.11826)

  - 提出数据集Occluded-Vehicles

- [CVPR2020] [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://arxiv.org/pdf/2003.04490.pdf) [[Code]](https://github.com/AdamKortylewski/CompositionalNets)

  - 使用数据集Occluded-Vehicles



### Stereo

- [CVPR2001] [Handling occlusions in dense multi-view stereo](https://ieeexplore.ieee.org/abstract/document/990462)

- [CVPR2005] [Symmetric stereo matching for occlusion handling](https://ieeexplore.ieee.org/abstract/document/1467470)

- [BMVC2018] [Symmnet: A symmetric convolutional neural network for occlusion detection](https://arxiv.org/abs/1807.00959)



### Object Tracking

- [ICPR2004] [Tracking people through occlusions](https://ieeexplore.ieee.org/abstract/document/1334361)

- [CVPR2005] [Real-time multiple objects tracking with occlusion handling in dynamic scenes](https://ieeexplore.ieee.org/abstract/document/1467371)

- [CVPR2007] [Robust Occlusion Handling in Object Tracking](https://ieeexplore.ieee.org/abstract/document/4270451)

- [ICME2014] [Multi-person tracking-by-detection with local particle filtering and global occlusion handling](https://ieeexplore.ieee.org/abstract/document/6890149)

- [TC2017] [Towards occlusion handling: object tracking with background estimation](https://ieeexplore.ieee.org/abstract/document/7994708)

- [TMM2018] [Context-aware three-dimensional mean-shift with occlusion handling for robust object tracking in RGB-D videos](https://ieeexplore.ieee.org/abstract/document/8425768/)

- [2020] [A Bayesian Filter for Multi-view 3D Multi-object Tracking with Occlusion Handling](https://arxiv.org/abs/2001.04118)

  

### Others

- [CVPR2011] [Monocular 3D scene understanding with explicit occlusion reasoning](https://ieeexplore.ieee.org/abstract/document/5995547)

- [CVPR2013] [Explicit occlusion modeling for 3d object class representations](https://openaccess.thecvf.com/content_cvpr_2013/html/Zia_Explicit_Occlusion_Modeling_2013_CVPR_paper.html)

- [IROS2018] [Instance segmentation of visible and occluded regions for finding and picking target from a pile of objects](https://ieeexplore.ieee.org/abstract/document/8593690)

- [3DV2018] [Seethrough: Finding objects in heavily occluded indoor scene images](https://ieeexplore.ieee.org/abstract/document/8490977)

- [IROS2019] [Seeing behind things: Extending semantic segmentation to occluded regions](https://arxiv.org/abs/1906.02885)



## Some Opinions

- 方向：GAN、amodal segmentation、compositional models (遮挡补全、或者利用局部特征和背景)

- 没有专用于occlusion的大规模目标检测数据集，没有不同程度的遮挡情况，没有统一的标准benchmark。大部分论文的实验数据都是人为模拟合成，数据较为单一简单。

- 在实际应用中，第一步需判断目标是否被遮挡。虽然这个问题可以用amodal segmentation解决，但是标注不精准并且耗时耗力。