Measuring Collectiveness in Crowded Scenes via Link Prediction

DOI Web Site 参考文献10件
  • JIANG Jun
    College of Electronics and Information Engineering, Sichuan University School of Computer Science, Southwest Petroleum University
  • WU Di
    College of Electronics and Information Engineering, Sichuan University
  • TENG Qizhi
    College of Electronics and Information Engineering, Sichuan University
  • HE Xiaohai
    College of Electronics and Information Engineering, Sichuan University
  • GAO Mingliang
    School of Electrical & Electronic Engineering, Shandong University of Technology

抄録

Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.

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