行人轨迹预测方法关键问题研究：现状及展望

杜泉成; 王晓; 李灵犀; 宁焕生

doi:10.11959/j.issn.2096-6652.202315

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行人轨迹预测方法关键问题研究：现状及展望

综述与展望 | 更新时间：2024-04-04

- 行人轨迹预测方法关键问题研究：现状及展望
- Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects
- 智能科学与技术学报 2023年5卷第2期页码：143-162
- 作者机构：
  
  1. 北京科技大学计算机与通信工程学院，北京 100083
  2. 安徽大学人工智能学院，安徽合肥 230601
  3. 青岛智能产业技术研究院，山东青岛 266109
  4. 美国印第安纳大学-普渡大学印第安纳波利斯联合分校电子与计算机工程系，美国印第安纳州 IN 46204
- 作者简介：
  
  [ "杜泉成（1994- ），男，北京科技大学计算机与通信工程学院博士生，主要研究方向为轨迹预测和车辆规划决策" ]
  [ "王晓（1988- ），女，博士，安徽大学人工智能学院教授，青岛智能产业技术研究院院长，主要研究方向为社交网络分析、社交交通、网络运动组织和多智能体建模" ]
  [ "李灵犀（1977- ），男，博士，美国印第安纳大学−普渡大学印第安纳波利斯联合分校电子与计算机工程系教授，主要研究方向为复杂系统的建模、分析、控制与优化，互联与自动化车辆，智能交通系统，智能车辆，离散事件动态系统和人机交互" ]
  [ "宁焕生（1975- ），男，博士，北京科技大学计算机与通信工程学院教授，主要研究方向为网络空间及物联网研究" ]
- 基金信息：
  
  国家自然科学基金项目;The National Natural Science Foundation of China(U1811463);国家自然科学基金项目(62173329)
- DOI：10.11959/j.issn.2096-6652.202315
  中图分类号： TP29
- 修回：2023-03-24，
  
  网络首发：2023-06，
  
  纸质出版：2023-06-15
- 稿件说明：
移动端阅览
杜泉成, 王晓, 李灵犀, 等. 行人轨迹预测方法关键问题研究：现状及展望[J]. 智能科学与技术学报, 2023,5(2):143-162.

Quancheng DU, Xiao WANG, Lingxi LI, et al. Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 143-162.
杜泉成, 王晓, 李灵犀, 等. 行人轨迹预测方法关键问题研究：现状及展望[J]. 智能科学与技术学报, 2023,5(2):143-162. DOI： 10.11959/j.issn.2096-6652.202315.

Quancheng DU, Xiao WANG, Lingxi LI, et al. Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 143-162. DOI： 10.11959/j.issn.2096-6652.202315.

摘要

行人轨迹预测旨在利用观察到的人类历史轨迹和周围环境信息来预测目标行人未来的位置信息，该研究具有重要的应用价值，可以降低自动驾驶车辆在社会交互下的碰撞风险。然而，传统的模型驱动的行人轨迹预测方法难以在复杂高动态的场景下对行人进行轨迹预测。相比之下，数据驱动的行人轨迹预测方法依靠大规模数据集平台，可以更好地捕捉和建模更复杂的行人交互关系，进而取得较精准的行人轨迹预测效果，成为自动驾驶、机器人导航和视频监控等领域的研究热点。为了宏观把握行人轨迹预测方法的研究现状及关键问题，以行人轨迹预测技术和方法分类为切入点，首先，详述行人轨迹预测已有方法的研究进展并归纳了目前存在的关键问题与挑战；其次，根据行人轨迹预测模型的建模差异，将现有方法分为模型驱动和数据驱动的行人轨迹预测方法，同时总结了不同方法的优缺点及适用场景；然后，对行人轨迹预测任务中使用的主流数据集进行了归纳总结，并对比了不同算法的性能指标；最后，针对行人轨迹预测的未来发展方向进行了展望。

Abstract

Pedestrian trajectory prediction aims to use observed human historical trajectories and surrounding environmental information to predict the future position of the target pedestrian

which has important application value in reducing collision risks for autonomous vehicles in social interactions.However

traditional model-driven pedestrian trajectory prediction methods are difficult to predict pedestrian trajectories in complex and highly dynamic scenes.In contrast

datadriven pedestrian trajectory prediction methods rely on large-scale datasets and can better capture and model more complex pedestrian interaction relationships

thereby achieving more accurate pedestrian trajectory prediction results

and have become a research hotspot in fields such as autonomous driving

robot navigation and video surveillance.In order to macroscopically grasp the research status and key issues of pedestrian trajectory prediction methods

We started with the classification of pedestrian trajectory prediction technology and methods.First

the research progress of existing pedestrian trajectory prediction methods were elaborated and the current key issues and challenges were summarized.Second

according to the modeling differences of pedestrian trajectory prediction models

existing methods were divided into model-driven and data-driven pedestrian trajectory prediction methods

and the advantages

disadvantages and applicable scenarios of different methods were summarized.Then

the mainstream datasets used in pedestrian trajectory prediction tasks were summarized and the performance indicators of different algoriths were compared.Finally

the future development direction of pedestrian trajectory prediction was prospected.

关键词

Keywords

references

SHARMA N , DHIMAN C , INDU S . Pedestrian intention prediction for autonomous vehicles:a comprehensive survey [J ] . Neurocomputing , 2022 , 508 : 120 - 152 .

CAESAR H , BANKITI V , LANG A H , et al . nuScenes:a multimodal dataset for autonomous driving [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 11618 - 11628 .

RUDENKO A , PALMIERI L , HERMAN M , et al . Human motion trajectory prediction:a survey [J ] . The International Journal of Robotics Research , 2020 , 39 ( 8 ): 895 - 935 .

RÖSMANN C , OELJEKLAUS M , HOFFMANN F , et al . Online trajectory prediction and planning for social robot navigation [C ] // Proceedings of 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) . Piscataway:IEEE Press , 2017 : 1255 - 1260 .

BALLAN L , CASTALDO F , ALAHI A , et al . Knowledge transfer for scene-specific motion prediction [M ] . Computer Vision - ECCV 2016 . Cham : Springer International Publishing , 2016 : 697 - 713 .

SIGHENCEA B I , STANCIU R I , CĂLEANU C D . A review of deep learning-based methods for pedestrian trajectory prediction [J ] . Sensors , 2021 , 21 ( 22 ): 7543 .

孔玮 , 刘云 , 李辉 , 等 . 基于深度学习的行人轨迹预测方法综述 [J ] . 控制与决策 , 2021 , 36 ( 12 ): 2841 - 2850 .

KONG W , LIU Y , LI H , et al . Survey of pedestrian trajectory prediction methods based on deep learning [J ] . Control and Decision , 2021 , 36 ( 12 ): 2841 - 2850 .

KORBMACHER R , TORDEUX A . Review of pedestrian trajectory prediction methods:comparing deep learning and knowledge-based approaches [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 12 ): 24126 - 24144 .

陈敏 , 曾凯 , 沈韬 , 等 . 基于注意力机制和稀疏图卷积的行人轨迹预测 [J ] . 激光与光电子学进展 , 2023 , 60 ( 10 ): 1010013 .

CHEN M , ZENG K , SHEN T , et al . Pedestrian trajectory prediction based on attention mechanism and sparse graph convolution [J ] . Laser and Optoelectronics Progress , 2023 , 60 ( 10 ): 1010013 .

XU Y , WANG L C , WANG Y Z , et al . Adaptive trajectory prediction via transferable GNN [C ] // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2022 : 6510 - 6521 .

裴炤 , 邱文涛 , 王淼 , 等 . 基于Transformer动态场景信息生成对抗网络的行人轨迹预测方法 [J ] . 电子学报 , 2022 , 50 ( 7 ): 1537 - 1547 .

PEI Z , QIU W T , WANG M , et al . Pedestrian trajectory prediction method using dynamic scene infor? mation based transformer generative adversarial network [J ] . Acta Electronica Sinica , 2022 , 50 ( 7 ): 15371547 .

HELBING D , MOLNÁR P . Social force model for pedestrian dynamics [J ] . Physical Review E , 1995 , 51 ( 5 ): 4282 - 4286 .

YAN X . Modeling local behavior for predicting social interactions towards human tracking [J ] . Pattern Recognition , 2014 , 47 ( 4 ): 1626 - 1641 .

HELBING D , FARKAS I , VICSEK T . Simulating dynamical features of escape panic [J ] . Nature , 2000 , 407 ( 6803 ): 487 - 490 .

SCHÖLLER C , ARAVANTINOS V , LAY F , et al . What the constant velocity model can teach us about pedestrian motion prediction [J ] . IEEE Robotics and Automation Letters , 2020 , 5 ( 2 ): 1696 - 1703 .

FOX E , SUDDERTH E B , JORDAN M I , et al . Bayesian nonparametric inference of switching dynamic linear models [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 4 ): 1569 - 1585 .

KOOIJ J F P , SCHNEIDER N , FLOHR F , et al . Context-based pedestrian path prediction [M ] // Computer Vision - ECCV 2014 . Cham : Springer International Publishing , 2014 : 618 - 633 .

SCHNEIDER N , GAVRILA D M . Pedestrian path prediction with recursive Bayesian filters:a comparative study [M ] // Lecture Notes in Computer Science . Berlin,Heidelberg : Springer Berlin Heidelberg , 2013 : 174 - 183 .

DENDORFER P , ELFLEIN S , LEAL-TAIXÉ L . MG-GAN:a multigenerator model preventing out-of-distribution samples in pedestrian trajectory prediction [C ] // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2022 : 13138 - 13147 .

GIULIARI F , HASAN I , CRISTANI M , et al . Transformer networks for trajectory forecasting [C ] // Proceedings of 2020 25th International Conference on Pattern Recognition (ICPR) . Piscataway:IEEE Press , 2021 : 10335 - 10342 .

GUPTA A , JOHNSON J , LI F F , et al . Social GAN:socially acceptable trajectories with generative adversarial networks [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 2255 - 2264 .

ALAHI A , GOEL K , RAMANATHAN V , et al . Social LSTM:human trajectory prediction in crowded spaces [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2016 : 961 - 971 .

李琳辉 , 周彬 , 任威威 , 等 . 行人轨迹预测方法综述 [J ] . 智能科学与技术学报 , 2021 , 3 ( 4 ): 399 - 411 .

LI L H , ZHOU B , REN W W , et al . Review of pedestrian trajectory prediction methods [J ] . Chinese Journal of Intelligent Science and Technology , 2021 , 3 ( 4 ): 399 - 411 .

SHI X D , SHAO X W , GUO Z L , et al . Pedestrian trajectory prediction in extremely crowded scenarios [J ] . Sensors , 2019 , 19 ( 5 ): 1223 .

SEYFRIED A , STEFFEN B , KLINGSCH W , et al . The fundamental diagram of pedestrian movement revisited [J ] . Journal of Statistical Mechanics:Theory and Experiment , 2005 , 2005 ( 10 ): 10002 .

KENNEDY J , EBERHART R . Particle swarm optimization [C ] // Proceedings of ICNN'95 - International Conference on Neural Networks . Piscataway:IEEE Press , 2002 : 1942 - 1948 .

JIA H F , LIN Y , LUO Q Y , et al . Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm [J ] . Advances in Mechanical Engineering , 2019 , 11 ( 4 ): 168781401984249 .

MEHRAN R , OYAMA A , SHAH M . Abnormal crowd behavior detection using social force model [C ] // Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2009 : 935 - 942 .

PELLEGRINI S , ESS A , SCHINDLER K , et al . You'll never walk alone:modeling social behavior for multi-target tracking [C ] // Proceedings of 2009 IEEE 12th International Conference on Computer Vision . Piscataway:IEEE Press , 2010 : 261 - 268 .

CHOI W , SAVARESE S . A unified framework for multi-target tracking and collective activity recognition [M ] // Computer Vision – ECCV 2012 . Heidelberg : Springer Berlin Heidelberg , 2012 : 215 - 230 .

RUDENKO A , PALMIERI L , ARRAS K O . Joint long-term prediction of human motion using a planning-based social force approach [C ] // Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway:IEEE Press , 2018 : 4571 - 4577 .

TRAUTMAN P , KRAUSE A . Unfreezing the robot:navigation in dense,interacting crowds [C ] // Proceedings of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems . Piscataway:IEEE Press , 2010 : 797 - 803 .

PELLEGRINI S , ESS A , VAN GOOL L . Improving data association by joint modeling of pedestrian trajectories and groupings [M ] // Computer Vision - ECCV 2010 . Heidelberg : Springer Berlin Heidelberg , 2010 : 452 - 465 .

KARAMOUZAS I , HEIL P , VAN BEEK P , et al . A predictive collision avoidance model for pedestrian simulation [M ] // Motion in Games . Berlin,Heidelberg : Springer Berlin Heidelberg , 2009 : 41 - 52 .

ZHOU B L , WANG X G , TANG X O . Understanding collective crowd behaviors:learning a Mixture model of Dynamic pedestrian-Agents [C ] // Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2012 : 2871 - 2878 .

FOX E , SUDDERTH E B , JORDAN M I , et al . Bayesian nonparametric inference of switching dynamic linear models [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 4 ): 1569 - 1585 .

BEST G , FITCH R . Bayesian intention inference for trajectory prediction with an unknown goal destination [C ] // Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway:IEEE Press , 2015 : 5817 - 5823 .

XIE D , SHU T M , TODOROVIC S , et al . Learning and inferring "dark matter" and predicting human intents and trajectories in videos [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018 , 40 ( 7 ): 1639 - 1652 .

XUE H , HUYNH D Q , REYNOLDS M . SS-LSTM:a hierarchical LSTM model for pedestrian trajectory prediction [C ] // Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) . Piscataway:IEEE Press , 2018 : 1186 - 1194 .

MANH H , ALAGHBAND G . Scene-lstm:a model for human trajectory prediction [J ] . arXiv preprint , 2018 ,arXiv:1808.04018.

HASAN I , SETTI F , TSESMELIS T , et al . MX-LSTM:mixing tracklets and vislets to jointly forecast trajectories and head poses [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 6067 - 6076 .

BISAGNO N , ZHANG B , CONCI N . Group LSTM:group trajectory prediction in crowded scenarios [M ] // Lecture Notes in Computer Science . Cham : Springer International Publishing , 2019 : 213 - 225 .

CHENG B , XU X , ZENG Y J , et al . Pedestrian trajectory prediction via the Social-Grid LSTM model [J ] . The Journal of Engineering , 2018 ( 16 ): 1468 - 1474 .

ZHU Y L , QIAN D H , REN D C , et al . StarNet:pedestrian trajectory prediction using deep neural network in star topology [C ] // Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway:IEEE Press , 2020 : 8075 - 8080 .

LERNER A , CHRYSANTHOU Y , LISCHINSKI D . Crowds by example [J ] . Computer Graphics Forum , 2007 , 26 ( 3 ): 655 - 664 .

SADEGHIAN A , KOSARAJU V , SADEGHIAN A , et al . SoPhie:an attentive GAN for predicting paths compliant to social and physical constraints [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 1349 - 1358 .

AMIRIAN J , HAYET J B , PETTRÉ J . Social ways:learning multimodal distributions of pedestrian trajectories with GANs [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway:IEEE Press , 2020 : 2964 - 2972 .

HUANG L , ZHUANG J H , CHENG X M , et al . STI-GAN:multimodal pedestrian trajectory prediction using spatiotemporal interactions and a generative adversarial network [J ] . IEEE Access , 2021 , 9 : 50846 - 50856 .

LAI W C , XIA Z X , LIN H S , et al . Trajectory prediction in heterogeneous environment via attended ecology embedding [C ] // Proceedings of the 28th ACM International Conference on Multimedia . New York:ACM , 2020 : 202 - 210 .

KOSARAJU V , SADEGHIAN A , MARTÍN-MARTÍN R , , et al . SocialBiGAT:Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks [J ] . Advances in Neural Information Processing Systems , 2019 :32.

VELIČKOVIĆ P , CUCURULL G , CASANOVA A , et al . Graph Attention Networks [J ] . arXiv preprint , 2017 ,arXiv:1710.10903.

CHEN X , DUAN Y , HOUTHOOFT R , et al . InfoGAN:interpretable representation learning by information maximizing generative adversarial nets [C ] // Proceedings of the 30th International Conference on Neural Information Processing Systems . New York:ACM , 2016 : 2180 - 2188 .

FANG F , ZHANG P P , ZHOU B , et al . Atten-GAN:pedestrian trajectory prediction with GAN based on attention mechanism [J ] . Cognitive Computation , 2022 , 14 ( 6 ): 2296 - 2305 .

MOHAMED A , QIAN K , ELHOSEINY M , et al . Social-STGCNN:a social spatio-temporal graph convolutional neural network for human trajectory prediction [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 14412 - 14420 .

HUANG Y F , BI H K , LI Z X , et al . STGAT:modeling spatialtemporal interactions for human trajectory prediction [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2020 : 6271 - 6280 .

SHI L S , WANG L , LONG C J , et al . SGCN:sparse graph convolution network for pedestrian trajectory prediction [C ] // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2021 : 8990 - 8999 .

YAN S J , XIONG Y J , LIN D H . Spatial temporal graph convolutional networks for skeleton-based action recognition [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2018 , 32 ( 1 ): 12328 .

SUN J H , JIANG Q H , LU C W . Recursive social behavior graph for trajectory prediction [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 657 - 666 .

BAE I , JEON H G . Disentangled multi-relational graph convolutional network for pedestrian trajectory prediction [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2021 , 35 ( 2 ): 911 - 919 .

CADENA P R G , QIAN Y Q , WANG C X , et al . Pedestrian graph:a fast pedestrian crossing prediction model based on graph convolutional networks [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 11 ): 21050 - 21061 .

LYU P , WANG W , WANG Y , et al . SSAGCN:social soft attention graph convolution network for pedestrian trajectory prediction [J ] . arXiv preprint , 2021 ,arXiv:2112.02459.

ZHOU H , REN D C , XIA H X , et al . AST-GNN:an attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction [J ] . Neurocomputing , 2021 , 445 : 298 - 308 .

田永林 , 王雨桐 , 王建功 , 等 . 视觉Transformer研究的关键问题:现状及展望 [J ] . 自动化学报 , 2022 , 48 ( 4 ): 957 - 979 .

TIAN Y L , WANG Y T , WANG J G , et al . Key problems and progress of vision transformers:the state of the art and prospects [J ] . Acta Automatica Sinica , 2022 , 48 ( 4 ): 957 - 979 .

DEVLIN J , CHANG M W , LEE K , et al . BERT:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv preprint , 2018 ,arXiv:1810.04805.

RADFORD A , NARASIMHAN K Improving language understanding by generative pre-training [Z ] . 2018 .

WANG A , SINGH A , MICHAEL J , et al . GLUE:a multi-task benchmark and analysis platform for natural language understanding [J ] . arXiv preprint , 2018 ,arXiv:1804.07461.

CARION N , MASSA F , SYNNAEVE G , et al . End-to-end object detection with transformers [M ] // Computer Vision - ECCV 2020 . Cham : Springer International Publishing , 2020 : 213 - 229 .

DOSOVITSKIY A , BEYER L , KOLESNIKOV A , et al . An image is worth 16x16 words:transformers for image recognition at scale [J ] . arXiv preprint , 2020 ,arXiv:2010.11929.

FU J , LIU J , TIAN H J , et al . Dual attention network for scene segmentation [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 3141 - 3149 .

YAO H Y , WAN W G , LI X . End-to-end pedestrian trajectory forecasting with transformer network [J ] . ISPRS International Journal of GeoInformation , 2022 , 11 ( 1 ): 44 .

YU C J , MA X , REN J W , et al . Spatio-temporal graph transformer networks for pedestrian trajectory prediction [M ] // Computer Vision ECCV 2020 . Cham : Springer International Publishing , 2020 : 507 - 523 .

SALEH K . Pedestrian trajectory prediction using context-augmented transformer networks [J ] . arXiv preprint , 2020 ,arXiv:2012.01757.

YIN Z , LIU R , XIONG Z , et al . Multimodal transformer network for pedestrian trajectory prediction [C ] // IJCAI .[S.l.:s.n. ] , 2021 : 1259 - 1265 .

LI L H , PAGNUCCO M , SONG Y . Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction [C ] // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2022 : 2221 - 2231 .

SU Z X , HUANG G , ZHANG S Y , et al . Crossmodal transformer based generative framework for pedestrian trajectory prediction [C ] // Proceedings of 2022 International Conference on Robotics and Automation (ICRA) . Piscataway:IEEE Press , 2022 : 2337 - 2343 .

ROBICQUET A , SADEGHIAN A , ALAHI A , et al . Learning social etiquette:human trajectory understanding in crowded scenes [M ] // Computer Vision – ECCV 2016 . Cham : Springer International Publishing , 2016 : 549 - 565 .

OH S , HOOGS A , PERERA A , et al . A large-scale benchmark dataset for event recognition in surveillance video [C ] // Proceedings of CVPR . Piscataway:IEEE Press , 2011 : 3153 - 3160 .

GRIFFIN G , HOLUB A , PERONA P Caltech-256 object category dataset [Z ] . 2007 .

ELLIS A , FERRYMAN J . PETS2010 and PETS2009 evaluation of results using individual ground truthed single views [C ] // Proceedings of 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance . Piscataway:IEEE Press , 2010 : 135 - 142 .

SHAO S , ZHAO Z , LI B , et al . Crowdhuman:a benchmark for detecting human in a crowd [J ] . arXiv preprint , 2018 ,arXiv:1805.00123.

BOCK J , KRAJEWSKI R , MOERS T , et al . The InD dataset:a drone dataset of naturalistic road user trajectories at German intersections [C ] // Proceedings of 2020 IEEE Intelligent Vehicles Symposium (IV) . Piscataway:IEEE Press , 2021 : 1929 - 1934 .

KOTSERUBA I , RASOULI A , TSOTSOS J K . Joint attention in autonomous driving (JAAD) [J ] . arXiv preprint , 2016 ,arXiv:1609. 04741.

RASOULI A , KOTSERUBA I , KUNIC T , et al . PIE:a large-scale dataset and models for pedestrian intention estimation and trajectory prediction [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2020 : 6261 - 6270 .

CONG P , ZHU X , QIAO F , et al . Stcrowd:a multimodal dataset for pedestrian perception in crowded scenes [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2022 : 19608 - 19617 .

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