Measuring Collectiveness in Crowded Scenes via Link Prediction

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...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E98.D; no. 8; pp. 1617 - 1620
Main Authors JIANG, Jun, WU, Di, TENG, Qizhi, HE, Xiaohai, GAO, Mingliang
Format Journal Article
LanguageEnglish
Published The Institute of Electronics, Information and Communication Engineers 01.08.2015
Subjects
collectiveness
Communities
Computer vision
link prediction
Links
Networks
Random walk
Similarity
State of the art
Stems
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Summary: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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ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.2015EDL8011