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Conference Paper: Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction

TitleEncoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction
Authors
Issue Date2018
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, p. 5275-5284 How to Cite?
AbstractPedestrian trajectory prediction is a challenging task because of the complex nature of humans. In this paper, we tackle the problem within a deep learning framework by considering motion information of each pedestrian and its interaction with the crowd. Specifically, motivated by the residual learning in deep learning, we propose to predict displacement between neighboring frames for each pedestrian sequentially. To predict such displacement, we design a crowd interaction deep neural network (CIDNN) which considers the different importance of different pedestrians for the displacement prediction of a target pedestrian. Specifically, we use an LSTM to model motion information for all pedestrians and use a multi-layer perceptron to map the location of each pedestrian to a high dimensional feature space where the inner product between features is used as a measurement for the spatial affinity between two pedestrians. Then we weight the motion features of all pedestrians based on their spatial affinity to the target pedestrian for location displacement prediction. Extensive experiments on publicly available datasets validate the effectiveness of our method for trajectory prediction.
Persistent Identifierhttp://hdl.handle.net/10722/345244
ISSN
2023 SCImago Journal Rankings: 10.331

 

DC FieldValueLanguage
dc.contributor.authorXu, Yanyu-
dc.contributor.authorPiao, Zhixin-
dc.contributor.authorGao, Shenghua-
dc.date.accessioned2024-08-15T09:26:08Z-
dc.date.available2024-08-15T09:26:08Z-
dc.date.issued2018-
dc.identifier.citationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, p. 5275-5284-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://hdl.handle.net/10722/345244-
dc.description.abstractPedestrian trajectory prediction is a challenging task because of the complex nature of humans. In this paper, we tackle the problem within a deep learning framework by considering motion information of each pedestrian and its interaction with the crowd. Specifically, motivated by the residual learning in deep learning, we propose to predict displacement between neighboring frames for each pedestrian sequentially. To predict such displacement, we design a crowd interaction deep neural network (CIDNN) which considers the different importance of different pedestrians for the displacement prediction of a target pedestrian. Specifically, we use an LSTM to model motion information for all pedestrians and use a multi-layer perceptron to map the location of each pedestrian to a high dimensional feature space where the inner product between features is used as a measurement for the spatial affinity between two pedestrians. Then we weight the motion features of all pedestrians based on their spatial affinity to the target pedestrian for location displacement prediction. Extensive experiments on publicly available datasets validate the effectiveness of our method for trajectory prediction.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-
dc.titleEncoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CVPR.2018.00553-
dc.identifier.scopuseid_2-s2.0-85062831093-
dc.identifier.spage5275-
dc.identifier.epage5284-

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