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 language | English |
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Title of host publication | 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1186-1194 |
Number of pages | 9 |
ISBN (Electronic) | 9781538648865 |
ISBN (Print) | 9781538648872 |
DOIs | |
Publication status | Published - 3 May 2018 |
Event | 2018 IEEE Winter Conference on Applications of Computer Vision - Lake Tahoe, United States Duration: 12 Mar 2018 → 15 Mar 2018 Conference number: 18 http://wacv18.wacv.net/ |
Conference
Conference | 2018 IEEE Winter Conference on Applications of Computer Vision |
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Abbreviated title | WACV 2018 |
Country/Territory | United States |
City | Lake Tahoe |
Period | 12/03/18 → 15/03/18 |
Internet address |