Crowd Tracking with Dynamic Evolution of Group Structures

  • Feng Zhu
  • Xiaogang Wang
  • Nenghai Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

Crowd tracking generates trajectories of a set of particles for further analysis of crowd motion patterns. In this paper, we try to answer the following questions: what are the particles appropriate for crowd tracking and how to track them robustly through crowd. Different than existing approaches of computing optical flows, tracking keypoints or pedestrians, we propose to discover distinctive and stable mid-level patches and track them jointly with dynamic evolution of group structures. This is achieved through the integration of low-level keypoint tracking, mid-level patch tracking, and high-level group evolution. Keypoint tracking guides the generation of patches with stable internal motions, and also organizes patches into hierarchical groups with collective motions. Patches are tracked together through occlusions with spatial constraints imposed by hierarchical tree structures within groups. Coherent groups are dynamically updated through merge and split events guided by keypoint tracking. The dynamically structured patches not only substantially improve the tracking for themselves, but also can assist the tracking of any other target in the crowd. The effectiveness of the proposed approach is shown through experiments and comparison with state-of-the-art trackers.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Feng Zhu
    • 1
  • Xiaogang Wang
    • 2
    • 3
  • Nenghai Yu
    • 1
  1. 1.Department of Electronic Engineering and Information ScienceUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of Electronic EngineeringThe Chinese University of Hong KongHong KongChina
  3. 3.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

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