SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction
Document Type
Conference Proceeding
Publication Date
8-29-2019
Publication Title
2019 IEEE Intelligent Vehicles Symposium (IV)
First page number:
2504
Last page number:
2509
Abstract
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.
Keywords
Semantic scene segmentation; Trajectory prediction; Scen information; Human motion; Crowded environment; SS-LSTM; LSTM model
Disciplines
Electrical and Computer Engineering | Engineering
Language
English
Repository Citation
Syed, A.,
Morris, B. T.
(2019).
SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction.
2019 IEEE Intelligent Vehicles Symposium (IV)
2504-2509.
http://dx.doi.org/10.1109/IVS.2019.8813801
