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Measuring Collectiveness via Refined Topological Similarity

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Published:03 March 2016Publication History
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Abstract

Crowd system has motivated a surge of interests in many areas of multimedia, as it contains plenty of information about crowd scenes. In crowd systems, individuals tend to exhibit collective behaviors, and the motion of all those individuals is called collective motion. As a comprehensive descriptor of collective motion, collectiveness has been proposed to reflect the degree of individuals moving as an entirety. Nevertheless, existing works mostly have limitations to correctly find the individuals of a crowd system and precisely capture the various relationships between individuals, both of which are essential to measure collectiveness. In this article, we propose a collectiveness-measuring method that is capable of quantifying collectiveness accurately. Our main contributions are threefold: (1) we compute relatively accurate collectiveness by making the tracked feature points represent the individuals more precisely with a point selection strategy; (2) we jointly investigate the spatial-temporal information of individuals and utilize it to characterize the topological relationship between individuals by manifold learning; (3) we propose a stability descriptor to deal with the irregular individuals, which influence the calculation of collectiveness. Intensive experiments on the simulated and real world datasets demonstrate that the proposed method is able to compute relatively accurate collectiveness and keep high consistency with human perception.

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 12, Issue 2
      March 2016
      224 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/2837041
      Issue’s Table of Contents

      Copyright © 2016 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 March 2016
      • Accepted: 1 October 2015
      • Revised: 1 September 2015
      • Received: 1 April 2015
      Published in tomm Volume 12, Issue 2

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