SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction

Research output: Chapter in Book/Conference paperConference paper

36 Citations (Scopus)

Abstract

Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of the scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians but ignore the scene layouts in pedestrian trajectory prediction. In this paper, a novel hierarchical LSTM-based network is proposed to consider both the influence of social neighbourhood and scene layouts. Our SS-LSTM, which stands for Social-Scene-LSTM, uses three different LSTMs to capture person, social and scene scale information. We also use a circular shape neighbourhood setting instead of the traditional rectangular shape neighbourhood in the social scale. We evaluate our proposed method against two baseline methods and a state-of-art technique on three public datasets. The results show that our method outperforms other methods and that using circular shape neighbourhood improves the prediction accuracy.
Original languageEnglish
Title of host publication2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1186-1194
Number of pages9
ISBN (Electronic)9781538648865
ISBN (Print)9781538648872
DOIs
Publication statusPublished - May 2018
Event18th IEEE Winter Conference on Applications of Computer Vision - Lake Tahoe, United States
Duration: 12 Mar 201815 Mar 2018
http://wacv18.wacv.net/

Conference

Conference18th IEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2018
CountryUnited States
CityLake Tahoe
Period12/03/1815/03/18
Internet address

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Cite this

Xue, H., Huynh, D., & Reynolds, M. (2018). SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1186-1194). [8354239] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WACV.2018.00135