• DocumentCode
    2097055
  • Title

    Multiple Object Tracking Based on Adaptive Depth Segmentation

  • Author

    Parvizi, Ehsan ; Wu, Q. M Jonathan

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Windsor, Windsor, ON
  • fYear
    2008
  • fDate
    28-30 May 2008
  • Firstpage
    273
  • Lastpage
    277
  • Abstract
    In this paper, we propose a multiple object tracking algorithm in three-dimensional (3D) domain based on a state of the art, adaptive range segmentation method. The performance of segmentation processes has an important impact on the achieved tracking results. Furthermore, segmentation methods which perform best on intensity images will not necessarily achieve promising results when applied on depth images from a time-of-flight sensor. Here, the employed unique segmentation promises a real-time tracking analysis, having a significantly high preprocessing efficiency. Our experiments confirm the robustness, as well as efficiency of the proposed approach.
  • Keywords
    image segmentation; object detection; probability; tracking; 3D domain; adaptive depth image segmentation; multiple object tracking algorithm; probabilistic filtering technique; time-of-flight sensor; Cameras; Computer vision; Image edge detection; Image segmentation; Image sensors; Layout; Object detection; Robustness; Surveillance; Target tracking; 3D Tracking; Depth Segmentation; Depth Sensing; Object Detection; Time-of-Flight;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Robot Vision, 2008. CRV '08. Canadian Conference on
  • Conference_Location
    Windsor, Ont.
  • Print_ISBN
    978-0-7695-3153-3
  • Type

    conf

  • DOI
    10.1109/CRV.2008.21
  • Filename
    4562121