Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach

  • Weiyue Wang
  • Weiyao Lin
  • Yuanzhe Chen
  • Jianxin Wu
  • Jingdong Wang
  • Bin Sheng
Conference paper

DOI: 10.1007/978-3-319-10590-1_49

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)
Cite this paper as:
Wang W., Lin W., Chen Y., Wu J., Wang J., Sheng B. (2014) Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer, Cham

Abstract

This paper addresses the problem of detecting coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition. We first introduce a coarse-to-fine thermal-diffusion-based approach. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. Finally, these semantic regions are used to recognize activities in crowded scenes. Experiments on various videos demonstrate the effectiveness of our approach.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Weiyue Wang
    • 1
  • Weiyao Lin
    • 1
  • Yuanzhe Chen
    • 1
  • Jianxin Wu
    • 2
  • Jingdong Wang
    • 3
  • Bin Sheng
    • 4
  1. 1.Dept. Electronic Engr.Shanghai Jiao Tong Univ.China
  2. 2.National Key Laboratory for Novel Software TechnologyNanjing Univ.China
  3. 3.Microsoft ResearchBeijingChina
  4. 4.Dept. Computer Science & Engr.Shanghai Jiao Tong Univ.China

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