Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios

Kai Chen; Xiao Song; Hang Yu

doi:10.1007/978-981-15-1078-6_3

Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios

Kai Chen
, Xiao Song^*
, Hang Yu

^*此作品的通讯作者

网络空间安全学院

Beihang University
Tsinghua University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

3 引用（Scopus）

摘要

Pedestrian trajectory prediction is a challenging problem in the crowded and chaotic scenarios. Currently, the prediction error is still high because the input of Long Short-Term Memory (LSTM) network is a 1D vector, which cannot represent the spatial information of pedestrians. To tackle this, we propose to use tensors to represent the complex environmental information. Meanwhile, LSTM internal full connection is converted into full convolution to predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.

源语言	英语
主期刊名	Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings
编辑	Gary Tan, Yong Meng Teo, Axel Lehmann, Wentong Cai
出版商	Springer
页	29-39
页数	11
ISBN（印刷版）	9789811510779
DOI	https://doi.org/10.1007/978-981-15-1078-6_3
出版状态	已出版 - 2019
活动	19th Asia Simulation Conference, AsiaSim 2019 - Singapore, 新加坡期限: 30 10月 2019 → 1 11月 2019

出版系列

姓名	Communications in Computer and Information Science
卷	1094
ISSN（印刷版）	1865-0929
ISSN（电子版）	1865-0937

会议

会议	19th Asia Simulation Conference, AsiaSim 2019
国家/地区	新加坡
市	Singapore
时期	30/10/19 → 1/11/19

访问文件

10.1007/978-981-15-1078-6_3

其它文件与链接

链接到 Scopus 的出版物

引用此

Chen, K., Song, X., & Yu, H. (2019). Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios. 在 G. Tan, Y. M. Teo, A. Lehmann, & W. Cai (编辑), Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings (页码 29-39). (Communications in Computer and Information Science; 卷 1094). Springer. https://doi.org/10.1007/978-981-15-1078-6_3

Chen, Kai ; Song, Xiao ; Yu, Hang. / Conv-LSTM : Pedestrian Trajectory Prediction in Crowded Scenarios. Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings. 编辑 / Gary Tan ; Yong Meng Teo ; Axel Lehmann ; Wentong Cai. Springer, 2019. 页码 29-39 (Communications in Computer and Information Science).

@inproceedings{81e8edf2fa2943e98f6a038487bba7a4,

title = "Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios",

abstract = "Pedestrian trajectory prediction is a challenging problem in the crowded and chaotic scenarios. Currently, the prediction error is still high because the input of Long Short-Term Memory (LSTM) network is a 1D vector, which cannot represent the spatial information of pedestrians. To tackle this, we propose to use tensors to represent the complex environmental information. Meanwhile, LSTM internal full connection is converted into full convolution to predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.",

keywords = "Convolutional neural network, Pedestrian behavior, Trajectory prediction",

author = "Kai Chen and Xiao Song and Hang Yu",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 19th Asia Simulation Conference, AsiaSim 2019 ; Conference date: 30-10-2019 Through 01-11-2019",

year = "2019",

doi = "10.1007/978-981-15-1078-6\_3",

language = "英语",

isbn = "9789811510779",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "29--39",

editor = "Gary Tan and Teo, \{Yong Meng\} and Axel Lehmann and Wentong Cai",

booktitle = "Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings",

address = "德国",

}

Chen, K, Song, X & Yu, H 2019, Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios. 在 G Tan, YM Teo, A Lehmann & W Cai (编辑), Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings. Communications in Computer and Information Science, 卷 1094, Springer, 页码 29-39, 19th Asia Simulation Conference, AsiaSim 2019, Singapore, 新加坡, 30/10/19. https://doi.org/10.1007/978-981-15-1078-6_3

Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios. / Chen, Kai; Song, Xiao; Yu, Hang.
Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings. 编辑 / Gary Tan; Yong Meng Teo; Axel Lehmann; Wentong Cai. Springer, 2019. 页码 29-39 (Communications in Computer and Information Science; 卷 1094).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Conv-LSTM

T2 - 19th Asia Simulation Conference, AsiaSim 2019

AU - Chen, Kai

AU - Song, Xiao

AU - Yu, Hang

PY - 2019

Y1 - 2019

N2 - Pedestrian trajectory prediction is a challenging problem in the crowded and chaotic scenarios. Currently, the prediction error is still high because the input of Long Short-Term Memory (LSTM) network is a 1D vector, which cannot represent the spatial information of pedestrians. To tackle this, we propose to use tensors to represent the complex environmental information. Meanwhile, LSTM internal full connection is converted into full convolution to predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.

AB - Pedestrian trajectory prediction is a challenging problem in the crowded and chaotic scenarios. Currently, the prediction error is still high because the input of Long Short-Term Memory (LSTM) network is a 1D vector, which cannot represent the spatial information of pedestrians. To tackle this, we propose to use tensors to represent the complex environmental information. Meanwhile, LSTM internal full connection is converted into full convolution to predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.

KW - Convolutional neural network

KW - Pedestrian behavior

KW - Trajectory prediction

UR - https://www.scopus.com/pages/publications/85075679805

U2 - 10.1007/978-981-15-1078-6_3

DO - 10.1007/978-981-15-1078-6_3

M3 - 会议稿件

AN - SCOPUS:85075679805

SN - 9789811510779

T3 - Communications in Computer and Information Science

SP - 29

EP - 39

BT - Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings

A2 - Tan, Gary

A2 - Teo, Yong Meng

A2 - Lehmann, Axel

A2 - Cai, Wentong

PB - Springer

Y2 - 30 October 2019 through 1 November 2019

ER -

Chen K, Song X, Yu H. Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios. 在 Tan G, Teo YM, Lehmann A, Cai W, 编辑, Methods and Applications for Modeling and Simulation of Complex Systems - 19th Asia Simulation Conference, AsiaSim 2019, Proceedings. Springer. 2019. 页码 29-39. (Communications in Computer and Information Science). doi: 10.1007/978-981-15-1078-6_3

Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios

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