• DOI:10.1109/IVS.2019.8813801
Corpus ID: 201810581

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}
}

A. Syed, B. Morris

Published 2019

Computer Science

2019 IEEE Intelligent Vehicles Symposium (IV)

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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