Pedestrian Trajectory Prediction Using RNN Encoder-Decoder with Spatio-Temporal Attentions

Pedestrian Trajectory Prediction Using RNN Encoder-Decoder with Spatio-Temporal Attentions
Title:
Pedestrian Trajectory Prediction Using RNN Encoder-Decoder with Spatio-Temporal Attentions
Other Titles:
2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR)
Publication Date:
03 May 2019
Citation:
N. Bhujel, E. K. Teoh and W. Yau, "Pedestrian Trajectory Prediction Using RNN Encoder-Decoder with Spatio-Temporal Attentions," 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR), Singapore, 2019, pp. 110-114. doi: 10.1109/ICMSR.2019.8835478
Abstract:
Pedestrian motion are inherently multi-modal in nature influenced by presence of other human and physical objects in the environment. Trajectory prediction models need to address both human-human and human-space interaction issues. In this work, we leverage both pedestrians information and scene information of the navigation environment for jointly predicting trajectories of the pedestrian. We introduce a new Recurrent Neural Network based sequence model with attention mechanisms that address both human-human and human-space interaction challenges. The encoder encodes all the pedestrian trajectories and create a social context. The scene information of navigation environment is extracted using CNN and serves as a physical context for the model. Our approach utilizes physical and social attention mechanism to find semantic alignments between encoder and decoder. The social attention mechanism allow the model to look into similar step of pedestrian trajectory. The physical attention mechanism tells the model where and what to focus on the scene. Experiment on several datasets shows that the proposed approach which combine social and physical attention performs better than when this information is utilized independently.
License type:
PublisherCopyrights
Funding Info:
(1) SERC grant No. 162 25 00036 from the National Robotics Programme (NRP), Singapore and (2) Niraj is a recipient of A*STAR-SINGA scholarship
Description:
(C) 2019 IEEE.
ISBN:
978-1-7281-2223-6
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