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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