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

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,...

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Bibliographic Details
Published in2018 IEEE Winter Conference on Applications of Computer Vision (WACV) pp. 1186 - 1194
Main Authors Xue, Hao, Huynh, Du Q., Reynolds, Mark
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Computer architecture
Force
Layout
Logic gates
Predictive models
Shape
Trajectory
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Summary: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.
DOI:10.1109/WACV.2018.00135