Robust Moving Object Detection on Moving Platforms

  • Ming-Yu Shih
  • Bwo-Chau Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


Most moving object detection methods rely on approaches similar to background subtraction or frame differences that require camera to be fixed at a certain position. However, on mobile robots, a background model can not be maintained because of the camera motion introduced by the robot motion. To overcome such obstacle, some researchers proposed methods that use optical flow and stereo vision to detect moving objects on moving platforms. These methods work under a assumption that the areas belong to the interesting foreground moving objects are relatively small compare to the areas belong to the uninteresting background scene. However, in many situations, the moving objects may approach closely to the robot on which the camera is located. In such a case, the assumption of small foreground moving object will be violated. This paper presents a framework which shows that the small foreground moving object assumption could be relaxed. Further, it integrates the observations in motion field and image alignment to provide a robust moving object detection solution in unconstrained indoor environment.


Optical Flow Object Detection Video Surveillance Image Alignment Video Surveillance System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ming-Yu Shih
    • 1
  • Bwo-Chau Fu
    • 1
  1. 1.Advanced Technology Center, Information & Computer LaboratoriesIndustrial Technology Research InstituteChutung, HsinchiuTaiwan, R.O.C.

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