Chapter

Computer Vision – ECCV 2014

Volume 8694 of the series Lecture Notes in Computer Science pp 139-154

Crowd Tracking with Dynamic Evolution of Group Structures

  • Feng ZhuAffiliated withDepartment of Electronic Engineering and Information Science, University of Science and Technology of China
  • , Xiaogang WangAffiliated withDepartment of Electronic Engineering, The Chinese University of Hong KongShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
  • , Nenghai YuAffiliated withDepartment of Electronic Engineering and Information Science, University of Science and Technology of China

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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.