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Pedestrian Behavior Understanding and Prediction with Deep Neural Networks

  • Shuai Yi
  • Hongsheng Li
  • Xiaogang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9905)

Abstract

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.

Keywords

Receptive Field Association Strategy Displacement Volume Deep Neural Network Walking Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work was supported in part by the Ph.D. Programs Foundation of China under Grant 20130185120039, in part by the Hong Kong Innovation and Technology Support Programme under Grant ITS/221/13FP, in part by the National Natural Science Foundation of China under Grant 61371192 and Grant 61301269, and in part by the General Research Fund through the Research Grants Council, Hong Kong, under Grant CUHK14206114, Grant CUHK14205615, Grant CUHK419412, and Grant CUHK14203015.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Electronic EngineeringChinese University of Hong KongHong KongChina
  2. 2.Sensetime Group LimitedHong KongChina

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