In this paper, we propose the use of semantic segmentation to incorporate scene information for better understanding of human motion in crowded environments. Our proposed SSeg-LSTM method leverages SegNet, which is a semantic segmentation encoder-decoder architecture, to extract semantically meaningful scene features. We then train the Social Scene LSTM (SS-LSTM) model with the contextual information regarding dynamics, social neighborhood, and scene semantics to predict future trajectory points of pedestrians. Experimental evaluation on public datasets show better performance for SSeg-LSTM than SS-LSTM which highlights the utility of semantic encoding for trajectory prediction.
SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction
2019 IEEE Intelligent Vehicles Symposium (IV) ; 2504-2509
2019-06-01
3475711 byte
Conference paper
Electronic Resource
English
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