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https://github.com/xuehaouwa/Awesome-Trajectory-Prediction

List: Awesome-Trajectory-Prediction
Last synced: about 1 month ago
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Host: GitHub
URL: https://github.com/xuehaouwa/Awesome-Trajectory-Prediction
Owner: xuehaouwa
Created: 2018-11-29T01:30:17.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2023-01-31T21:06:47.000Z (almost 2 years ago)
Last Synced: 2024-05-20T01:01:30.543Z (7 months ago)
Size: 15.4 MB
Stars: 1,111
Watchers: 49
Forks: 233
Open Issues: 3
Metadata Files:
- Readme: README.md
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Awesome-person-re-identification - [github1 - Interaction-Aware-Trajectory-Prediction)] (Uncategorized / Uncategorized)
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README

        # Awesome-Trajectory-Prediction

![version](https://img.shields.io/badge/version-0.0.1-ff69b4.svg) ![LastUpdated](https://img.shields.io/badge/LastUpdated-2022.01.13-lightgrey.svg) ![topic](https://img.shields.io/badge/topic-trajectory--prediction-brightgreen.svg?logo=github) [![HitCount](http://hits.dwyl.io/xuehaouwa/Awesome-Trajectory-Prediction.svg)](http://hits.dwyl.io/xuehaouwa/Awesome-Trajectory-Prediction) [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



  



This is a list of useful information about trajectory prediction. Related papers, datasets and codes are included.

Maintainer - Hao Xue

## News

A PyPi package of [trajectory prediction tools](https://github.com/xuehaouwa/Trajectory-Prediction-Tools) is published!

## Contributing

Please feel free to [pull requests](https://github.com/xuehaouwa/Awesome-Trajectory-Prediction/pulls) to add new resources.

## Papers

#### RNN Related

- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, 2018, [code](https://github.com/locuslab/TCN)

- When will you do what? - Anticipating Temporal Occurrences of Activities, 2018 CVPR,   [code]( https://github.com/yabufarha/anticipating-activities)

- Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN, 2018 CVPR, [code](https://github.com/batzner/indrnn), [keras-code](https://github.com/titu1994/Keras-IndRNN), [pytorch code](https://github.com/StefOe/indrnn-pytorch/blob/master/indrnn.py)

- Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts, 2016 AAAI [code](https://github.com/yongqyu/STRNN)

#### Trajectory Prediction Related

- How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting, CVPR 2022, [Paper](https://arxiv.org/abs/2203.04781)

- Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation, ECCV 2022, [Paper](), [Code]()

- S-attack library: A library for evaluating trajectory prediction models [Website]()

- Vehicle trajectory prediction works, but not everywhere, arXiv 2021, [Paper](), [Code]()

- Are socially-aware trajectory prediction models really socially-aware? arXiv 2021, [Paper](), [Code]()

- Y-Net: From goals, waypoints & paths to long term human trajectory forecasting, ICCV 2021, [Paper](), [Code]() 

- LOKI: Long Term and Key Intentions for Trajectory Prediction, ICCV 2021, [Paper]()

- Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs, ICCV 2021 The ROAD Challenge Workshop, [Paper](), [Code]()

- MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction, ICCV 2021 [Paper](https://arxiv.org/abs/2108.09274), [code](https://github.com/selflein/MG-GAN)

- Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction, CVPR 2021 Oral [Paper](https://arxiv.org/abs/2104.08277)

- Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation, ACCV 2020 Oral [Paper](https://arxiv.org/abs/2010.01114), [code](https://github.com/dendorferpatrick/GoalGAN)

- Semantic Synthesis of Pedestrian Locomotion, ACCV 2020 [Paper](https://openaccess.thecvf.com/content/ACCV2020/html/Priisalu_Semantic_Synthesis_of_Pedestrian_Locomotion_ACCV_2020_paper.html)

- Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction， ECCV 2020 [Paper](https://arxiv.org/abs/2005.08514), [Code](https://github.com/Majiker/STAR)

- Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction, ECCV 2020 [Paper](https://arxiv.org/abs/2008.00777)

- It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction, ECCV 2020 Oral [Paper](https://arxiv.org/abs/2004.02025), [Code](https://github.com/HarshayuGirase/PECNet), [Homepage](https://karttikeya.github.io/publication/htf/)

- SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction, 2020, ECCV [Paper](https://arxiv.org/pdf/2007.13078.pdf)

- SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras, 2020 ECCV, [Paper](https://arxiv.org/pdf/2004.02022), [Code](https://github.com/JunweiLiang/Multiverse)

- Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction, 2020 CVPR, [Paper](), [Code]()

- The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction, 2020 CVPR, [Paper](https://arxiv.org/pdf/1912.06445.pdf), [Code](https://next.cs.cmu.edu/multiverse/index.html)

- Multiple Futures Prediction, 2019 NeurIPS, [Paper](http://papers.nips.cc/paper/9676-multiple-futures-prediction.pdf)

- Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision, 2020 WACV, [Paper](https://arxiv.org/abs/1911.01138)

- Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs, 2019 arXiv, [Paper](https://arxiv.org/pdf/1912.01118.pdf), [Code](https://gamma.umd.edu/researchdirections/autonomousdriving/spectralcows/)

- PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction, 2019 ICCV, [Paper]()

- STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction, 2019 ICCV, [Paper]()

- RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs, 2019 ACM CSCS, [Paper](https://arxiv.org/pdf/1907.08752.pdf), [Code](https://github.com/rohanchandra30/TrackNPred)

- Social and Scene-Aware Trajectory Prediction in Crowded Spaces, 2019 ICCV Workshop, [Paper](), [Code]()

- Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids, 2019 ArXiv, [Paper]()

- Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM, 2019 ISVC, [Paper](https://arxiv.org/pdf/1908.08908.pdf)

- SEABIG: A Deep Learning-Based Method for Location Prediction in Pedestrian Semantic Trajectories, 2019 IEEE Access, [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8790746)

- Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection, 2019 ISARC, [Paper](https://search.proquest.com/openview/f6b42779cd7037405799f86f8ca9e544/1?pq-origsite=gscholar&cbl=1646340&casa_token=KxabXy5827MAAAAA:Kv6jfuGvLfpCRhZb1YlzEB0pgpSFJQPKAG8yEcLUwZk4yVjYWn1iCKR1uesqbnH76XJj6smFh5Q)

- A novel model based on deep learning for Pedestrian detection and Trajectory prediction, 2019 ITAIC, [Paper](https://ieeexplore.ieee.org/abstract/document/8785741)

- Pedestrian Trajectory Prediction Using a Social Pyramid, 2019 PRICAI

- Path predictions using object attributes and semantic environment, 2019 VISIGRAPP, [Paper](http://mprg.jp/data/MPRG/C_group/C20190225_minoura.pdf)

- Probabilistic Path Planning using Obstacle Trajectory Prediction, 2019 [CoDS-COMAD '19](http://cods-comad.in/2019/index.html), [Paper](https://dl.acm.org/citation.cfm?id=3297006)

- Human Trajectory Prediction using Adversarial Loss, 2019 (from Alahi, conference unknown for now), [Paper](http://www.strc.ch/2019/Kothari_Alahi.pdf)

- Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories

  with GANs, 2019 CVPR [*Precognition Workshop*](https://sites.google.com/view/ieeecvf-cvpr2019-precognition), [Paper](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Amirian_Social_Ways_Learning_Multi-Modal_Distributions_of_Pedestrian_Trajectories_With_GANs_CVPRW_2019_paper.pdf), [Code]()

- Peeking into the Future: Predicting Future Person Activities and Locations in Videos, 2019 CVPR, [Paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liang_Peeking_Into_the_Future_Predicting_Future_Person_Activities_and_Locations_CVPR_2019_paper.pdf), [Code](https://github.com/google/next-prediction)

- Learning to Infer Relations for Future Trajectory Forecast, 2019 CVPR [*Precognition Workshop*](https://sites.google.com/view/ieeecvf-cvpr2019-precognition), [Paper](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Choi_Learning_to_Infer_Relations_for_Future_Trajectory_Forecast_CVPRW_2019_paper.pdf)

- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, 2019 CVPR, [Paper](), [Code](https://github.com/rohanchandra30/TrackNPred)

- Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes, 2019 CVPR, [Paper]()

- Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction, 2019 CVPR, [Paper](), [Code(TensorFlow)](https://github.com/lmb-freiburg/Multimodal-Future-Prediction)

- SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints, 2019 CVPR, [Paper]()

- Multi-Agent Tensor Fusion for Contextual Trajectory Prediction, 2019 CVPR, [Paper]()

- Future Person Localization in First-Person Videos, 2018 CVPR, [Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yagi_Future_Person_Localization_CVPR_2018_paper.pdf), [code](https://github.com/takumayagi/fpl) 

- Move, Attend and Predict: Anattention-based neural model for people’s movement prediction, 2018 Pattern Recognition Letters, [Paper](https://reader.elsevier.com/reader/sd/pii/S016786551830182X?token=1EF2B664B70D2B0C3ECDD07B6D8B664F5113AEA7533CE5F0B564EF9F4EE90D3CC228CDEB348F79FEB4E8CDCD74D4BA31)

- Group LSTM: Group Trajectory Prediction in Crowded Scenarios, 2018 ECCV Workshop, [Paper](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Bisagno_Group_LSTM_Group_Trajectory_Prediction_in_Crowded_Scenarios_ECCVW_2018_paper.pdf)

- Pedestrian Trajectory Prediction in Extremely Crowded Scenarios, 2019 Sensors (journal), [Paper](https://www.mdpi.com/1424-8220/19/5/1223/pdf)

- The Simpler the Better: Constant Velocity for Pedestrian Motion Prediction, 2019, [Paper](https://arxiv.org/pdf/1903.07933.pdf)

- SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction, 2019 CVPR, [Paper](https://arxiv.org/pdf/1903.02793.pdf)

- Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model, 2019 Computer Vision Winter Workshop (CVWW), [Paper](https://arxiv.org/pdf/1902.05437.pdf)

- Depth Information Guided Crowd Counting for Complex Crowd Scenes, 2018

- GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds, 2018 ACCV, [Paper](https://arxiv.org/pdf/1812.07667.pdf), [Demo](https://www.youtube.com/watch?v=7cCIC_JIfms)

- Tracking by Prediction: A Deep Generative Model for Mutli-Person Localisation and Tracking, 2018 WACV

- “Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention, 2018 WACV

- Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks, 2018 CVPR, [code](https://github.com/agrimgupta92/sgan)

- Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty, 2018 CVPR, [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Bhattacharyya_Long-Term_On-Board_Prediction_CVPR_2018_paper.pdf), [code+data](https://github.com/apratimbhattacharyya18/onboard_long_term_prediction)

- Encoding Crowd Interaction with Deep Neural Network

  for Pedestrian Trajectory Prediction, 2018 CVPR, [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Encoding_Crowd_Interaction_CVPR_2018_paper.pdf), [code](https://github.com/ShanghaiTechCVDL/CIDNN)

- Scene-LSTM: A Model for Human Trajectory Prediction, 2018 ArXiv, [Paper](https://arxiv.org/pdf/1808.04018.pdf)

- Bi-prediction: pedestrian trajectory prediction based on bidirectional LSTM classification, 2017 DICTA, [Paper]()

- Human Trajectory Prediction using Spatially aware Deep Attention Models, 2017 arxiv, [[Paper]](https://arxiv.org/pdf/1705.09436.pdf)

- Context-Aware Trajectory Prediction, 2017 arxiv, [[Paper]](https://arxiv.org/pdf/1705.02503.pdf)

- Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection, 2017 arxiv, [[Paper]](https://arxiv.org/pdf/1702.05552.pdf) 

- Forecasting Interactive Dynamics of Pedestrians with Fictitious Play, 2017 CVPR, [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ma_Forecasting_Interactive_Dynamics_CVPR_2017_paper.pdf)

- Social LSTM: Human Trajectory Prediction in Crowded Spaces, 2016 CVPR, [[Paper]](http://cvgl.stanford.edu/papers/CVPR16_Social_LSTM.pdf)

- STF-RNN: Space Time Features-based Recurrent Neural Network for predicting People Next Location, 2016 SSCI, [Keras-Code](https://github.com/mhjabreel/STF-RNN)

#### Vehicle Trajectory Prediction

- Motion Transformer with Global Intention Localization and Local Movement Refinement, 2022 NeurIPS, [Paper](https://arxiv.org/pdf/2209.13508.pdf), [Code](https://github.com/sshaoshuai/MTR)

- MPA: MultiPath++ Based Architecture for Motion Prediction, CVPR2022 Workshop on Autonomous Driving, [Paper](https://arxiv.org/pdf/2206.10041.pdf), [Code](https://github.com/stepankonev/waymo-motion-prediction-challenge-2022-multipath-plus-plus)

- PTNet: Physically Feasible Vehicle Trajectory Prediction, NeurIPS ML4AD Workshop, [paper]() 

- Injecting Knowledge in Data-driven Vehicle Trajectory Predictors, 2021 Transportation research part C, [Website](https://mohammadhossein-bahari.github.io/RRB/), [Paper](https://arxiv.org/pdf/2103.04854.pdf), [Code](https://github.com/vita-epfl/RRB)

- Multiple Futures Prediction, 2019 NeurIPS, [Paper](http://papers.nips.cc/paper/9676-multiple-futures-prediction.pdf)

- Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs, 2019 arXiv, [Paper](https://arxiv.org/pdf/1912.01118.pdf), [Code](https://gamma.umd.edu/researchdirections/autonomousdriving/spectralcows/)

- RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs, 2019 ACM CSCS, [Paper](https://arxiv.org/pdf/1907.08752.pdf), [Code](https://github.com/rohanchandra30/TrackNPred)

- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, 2019 CVPR, [Paper](), [Code](https://github.com/rohanchandra30/TrackNPred)

- Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks, 2019 CVPR, [Paper](https://arxiv.org/pdf/1812.09395.pdf)

- Argoverse: 3D Tracking and Forecasting With Rich Maps, 2019 CVPR, [Paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf)

- Robust Aleatoric Modeling for Future Vehicle Localization, 2019 CVPR [*Precognition Workshop*](https://sites.google.com/view/ieeecvf-cvpr2019-precognition), [Paper](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Hudnell_Robust_Aleatoric_Modeling_for_Future_Vehicle_Localization_CVPRW_2019_paper.pdf)

- Convolutional Social Pooling for Vehicle Trajectory Prediction [Paper](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w29/Deo_Convolutional_Social_Pooling_CVPR_2018_paper.pdf) [code](https://github.com/nachiket92/conv-social-pooling)

#### Traffic Prediction

- Deep Sequence Learning with Auxiliary Information for Traffic Prediction, 2018 KDD, [Paper](https://arxiv.org/pdf/1806.07380.pdf), [Code](https://github.com/JingqingZ/BaiduTraffic)

#### Others

- MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction [Code](https://github.com/xuehaouwa/POI-TForecast)

## Datasets

* [UCY](https://graphics.cs.ucy.ac.cy/research/downloads/crowd-data)

* [ETH](http://www.vision.ee.ethz.ch/en/datasets/)

* [New York Grand Central](http://www.ee.cuhk.edu.hk/en-gb/~syi/cvpr2015_dataset_pedestrianWalkingPath.pdf) (404)

* [Central Station](http://www.ee.cuhk.edu.hk/~xgwang/grandcentral.html)

* [Town Center](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html#datasets)

* [Edinburgh Informatics Forum Pedestrian Database](http://homepages.inf.ed.ac.uk/rbf/FORUMTRACKING/)

* [Cityscapes](https://www.cityscapes-dataset.com/login/)

* [Stanford Drone Dataset](http://cvgl.stanford.edu/projects/uav_data/)

* [Argoverse](https://www.argoverse.org/)

* [TRAF](https://gamma.umd.edu/researchdirections/autonomousdriving/trafdataset)

* [inD](https://www.ind-dataset.com/)

* [Lyft Level 5](https://level5.lyft.com/dataset/)

* [Apolloscape](http://apolloscape.auto/trajectory.html)