SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction
@article{Syed2019SSegLSTMSS, title={SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction}, author={A. Syed and B. Morris}, journal={2019 IEEE Intelligent Vehicles Symposium (IV)}, year={2019}, pages={2504-2509} }
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… CONTINUE READING
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