Method on Trajectory Collection for Pedestrians in Subway Station Based on Deep Learning Algorithm

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Method on Trajectory Collection for Pedestrians in Subway Station Based on Deep Learning Algorithm

Shi, Yihan / Xu, Jie / Zhang, Hui

The simulation models on pedestrian moving are of great importance to the study of pedestrian dynamics in subway stations. Due to the lack of high-precision pedestrian motion data, these improved models are rarely verified and not always reliable. The trajectories obtained by manually annotating the individual locations frame by frame are high-precision, but this method is difficult to perform when the population density is high. Based on the deep learning algorithm, this paper innovatively proposes a more efficient method on trajectory collection for pedestrians in subway stations. Firstly, by analyzing the characteristics of video on pedestrian flows in subway stations, the YOLOv4 is selected as the object detector and the Deep SORT algorithm is selected as the tracker for tracking pedestrians. Then the conversion process between pixel coordinates and world coordinates are introduced to obtain the trajectories in reality. To verify the proposed method, the observed trajectories are compared with the ground-truth trajectories. The result show that they were basically consistent and there has the MPE (Mean Position Error) ~ N (0.37, 0.0982). Finally, from the perspective of trajectory similarity and moving parameters, the SFM (Social Force Model) considering overtaking behavior is verified and proved to be more effective than the original and can reproduce the more realistic pedestrian moving process.

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Dokumentinformationen

Titel :

Method on Trajectory Collection for Pedestrians in Subway Station Based on Deep Learning Algorithm

Weitere Titelangaben:

Lect. Notes Electrical Eng.

Beteiligte:

Liang, Jianying (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Qin, Yong (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Shi, Yihan (Autor:in) / Xu, Jie (Autor:in) / Zhang, Hui (Autor:in)

Kongress:

International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021

Erschienen in:

Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021 ; Kapitel : 43 ; 389-396

Lecture Notes in Electrical Engineering ; 867

Erscheinungsdatum :

2022-02-19

Format / Umfang :

8 pages

ISBN :

978-981-16-9909-2 , 978-981-16-9908-5

ISSN :