Pedestrian Trajectory Prediction in Extremely Crowded Scenarios

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learni...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 19; H. 5; S. 1223
Hauptverfasser:	Shi, Xiaodan, Shao, Xiaowei, Guo, Zhiling, Wu, Guangming, Zhang, Haoran, Shibasaki, Ryosuke
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI 11.03.2019 MDPI AG
Schlagworte:	crowded scenarios deep learning encoder–decoder model human interaction LSTM trajectory prediction trajectory prediction deep learning human interaction LSTM crowded scenarios encoder–decoder model
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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