• DocumentCode
    2643054
  • Title

    Crowd Analysis at Mass Transit Sites

  • Author

    Kilambi, Prahlad ; Masoud, Osama ; Papanikolopoulos, Nikolaos

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Minnesota Univ., Twin Cities, MN
  • fYear
    2006
  • fDate
    17-20 Sept. 2006
  • Firstpage
    753
  • Lastpage
    758
  • Abstract
    We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in realtime. There are no constraints on camera placement. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Results are provided for groups of various sizes moving in an unconstrained fashion in crowded scenes
  • Keywords
    Kalman filters; estimation theory; parameter estimation; traffic engineering computing; Kalman filtering; camera calibration; camera placement; crowd analysis; mass transit sites; parameter estimation; Calibration; Cameras; Cities and towns; Computer science; Filtering; Head; Kalman filters; Layout; Parameter estimation; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
  • Conference_Location
    Toronto, Ont.
  • Print_ISBN
    1-4244-0093-7
  • Electronic_ISBN
    1-4244-0094-5
  • Type

    conf

  • DOI
    10.1109/ITSC.2006.1706832
  • Filename
    1706832