Location-Velocity Attention for Pedestrian Trajectory Prediction

Research output: Chapter in Book/Conference paperConference paperpeer-review

22 Citations (Scopus)

Abstract

Pedestrian path forecasting is crucial in applications such as smart video surveillance. It is a challenging task because of the complex crowd movement patterns in the scenes. Most of existing state-of-the-art LSTM based prediction methods require rich context like labelled static obstacles, labelled entrance/exit regions and even the background scene. Furthermore, incorporating contextual information into trajectory prediction increases the computational overhead and decreases the generalization of the prediction models across different scenes. In this paper, we propose a joint Location-Velocity Attention LSTM based method to predict trajectories. Specifically, a module is designed to tweak the LSTM network and an attention mechanism is trained to learn to optimally combine the location
and the velocity information of pedestrians in the prediction process. We have evaluated our approach against other baselines and state-of-the-art methods on several publicly available datasets. The results show that it not only outperforms other prediction methods but it also has a good generalization ability.
Original languageEnglish
Title of host publication2019 IEEE Winter Conference on Applications of Computer Vision
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2038–2047
Number of pages10
ISBN (Electronic)9781728119755
DOIs
Publication statusPublished - 4 Mar 2019
Event2019 IEEE Winter Conference on Applications of Computer Vision - Waikoloa Village, United States
Duration: 7 Jan 201911 Jan 2019
Conference number: 19

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Conference

Conference2019 IEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2019
Country/TerritoryUnited States
CityWaikoloa Village
Period7/01/1911/01/19

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