Pedestrian Behavior Understanding and Prediction with Deep Neural Networks
Refereed conference paper presented and published in conference proceedings


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摘要In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance. A pedestrian behavior encoding scheme is designed to provide a general representation of walking paths, which can be used as the input and output of CNN. The proposed Behavior-CNN is trained with real-scene crowd data and then thoroughly investigated from multiple aspects, including the location map and location awareness property, semantic meanings of learned filters, and the influence of receptive fields on behavior modeling. Multiple applications, including walking path prediction, destination prediction, and tracking, demonstrate the effectiveness of Behavior-CNN on pedestrian behavior modeling.
著者Shuai Yi, Hongsheng Li, Xiaogang Wang
會議名稱European Conference on Computer Vision
會議開始日08.10.2016
會議完結日10.10.2016
會議地點Amsterdam
會議國家/地區荷蘭
會議論文集題名ECCV 2016: Computer Vision – ECCV 2016
出版年份2016
月份9
頁次263 - 279
語言美式英語

上次更新時間 2018-18-01 於 11:20