• DocumentCode
    3570125
  • Title

    Identifying social groups in pedestrian crowd videos

  • Author

    Chandran, Arun Kumar ; Loh Ai Poh ; Vadakkepat, Prahlad

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    A Non-recursive Motion Similarity Clustering (NMSC) algorithm is proposed to identify pedestrians traveling together in social groups. The clustering algorithm is unsupervised and can automatically identify social groups within a region of interest in a video. Social groups are identified using only pedestrian motion information by imposing motion parameter thresholds defined by social psychological principles. Social groups are identified without any prior training. In addition to detecting small social groups, NMSC also detects short-term groups (occurring for a few seconds) and social groups with sparsely distributed pedestrians. The real-time performance and group identification accuracy reveal that the proposed clustering algorithm performs better compared to existing algorithms even for scenes with a large number of pedestrians.
  • Keywords
    image classification; image motion analysis; pedestrians; psychology; social sciences computing; unsupervised learning; video signal processing; NMSC algorithm; group identification accuracy; motion parameter thresholds; nonrecursive motion similarity clustering algorithm; pedestrian crowd videos; pedestrian motion information; social psychological principles; sparsely distributed pedestrians; Clustering algorithms; Monitoring; Observers; Real-time systems; Tracking; Trajectory; Videos; Video processing; kappa scores; motion similarity clustering; pedestrian groups;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on
  • Type

    conf

  • DOI
    10.1109/ICAPR.2015.7050677
  • Filename
    7050677