SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction

@article{Syed2019SSegLSTMSS,
  title={SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction},
  author={Arsal Syed and Brendan Tran Morris},
  journal={2019 IEEE Intelligent Vehicles Symposium (IV)},
  year={2019},
  pages={2504-2509}
}
  • Arsal SyedB. Morris
  • Published 9 June 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… 

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