• DocumentCode
    394465
  • Title

    Multi moving people detection from binocular sequences

  • Author

    Ran, Yung ; Zheng, Qinfen

  • Author_Institution
    Center for Autom. Res., Maryland Univ., College Park, MD, USA
  • Volume
    3
  • fYear
    2003
  • fDate
    6-10 April 2003
  • Abstract
    A novel approach for detection of multiple moving objects from binocular video sequences is reported. First an efficient motion estimation method is applied to sequences acquired from each camera. The motion estimation is then used to obtain cross camera correspondence between the stereo pair. Next, background subtraction is achieved by fusion of temporal difference and depth estimation. Finally moving foregrounds are further segmented into moving object according to a distance measure defined in a 2.5D feature space, which is done in a hierarchical strategy. The proposed approach has been tested on several indoor and outdoor sequences. Preliminary experiments have shown that the new approach can robustly detect multiple partially occluded moving persons in a noisy background. Representative human detection results are presented.
  • Keywords
    image sequences; motion estimation; object detection; stereo image processing; background subtraction; binocular sequences; cross camera correspondence; depth estimation; distance measure; hierarchical strategy; human detection results; indoor sequences; motion estimation method; multi moving people detection; multiple partially occluded persons; noisy background; outdoor sequences; stereo pair; temporal difference; video sequences; Automation; Cameras; Data mining; Educational institutions; Flowcharts; Humans; Motion estimation; Object detection; Radio access networks; Video sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
  • ISSN
    1520-6149
  • Print_ISBN
    0-7803-7663-3
  • Type

    conf

  • DOI
    10.1109/ICASSP.2003.1199101
  • Filename
    1199101