• DocumentCode
    114664
  • Title

    Macroscopic analysis of crowd motion in video sequences

  • Author

    Nakhmani, Arie ; Surana, Amit ; Tannenbaum, Allen

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Alabama at Birmingham, Birmingham, AL, USA
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    1822
  • Lastpage
    1827
  • Abstract
    We use dynamic active contours driven by optimal mass transport optical flow to detect crowd behaviors, in particular crowd merging, splitting and collision events. The overall framework is variational, and thus one could very naturally formulate functionals which include geometric active contours together with optical flow ideas. This allows to fuse temporal and intensity distribution information explicitly into a single framework. From a networking point of view, we consider here macroscopic models as opposed to microscopic or mesoscopic approaches to crowd dynamics. Our experiments show high detection rate of macro crowd behaviors with complicated real world scenarios.
  • Keywords
    computational geometry; image fusion; image sequences; video signal processing; video surveillance; crowd behavior detection; crowd collision event; crowd dynamics; crowd merging event; crowd motion; crowd splitting event; detection rate; dynamic active contours; explicit intensity distribution information fusion; explicit temporal distribution information fusion; geometric active contours; macrocrowd behaviors; macroscopic analysis; optimal mass transport optical flow; real world scenarios; variational framework; video sequences; Active contours; Computational modeling; Level set; Mathematical model; Motion segmentation; Optical imaging; Tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7039663
  • Filename
    7039663