Measuring Collectiveness in Crowded Scenes via Link Prediction

Jun JIANG  Di WU  Qizhi TENG  Xiaohai HE  Mingliang GAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.8   pp.1617-1620
Publication Date: 2015/08/01
Publicized: 2015/05/14
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8011
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
collectiveness,  link prediction,  random walk,  

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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.