Robust Moving Object Detection on Moving Platforms

  • Ming-Yu Shih
  • Bwo-Chau Fu
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O.: A System for Video Surveillance and Monitoring, tech. report CMU-RI-TR-00-12, Robotics Institute, CMU (May 2000)Google Scholar
  2. 2.
    Zhou, X., Collins, R., Kanade, T., Metes, P.: A Master-Slave System to Acquire Biometric Imagery of Humans at Distance. ACM International Workshop on Video Surveillance (November 2003)Google Scholar
  3. 3.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)CrossRefGoogle Scholar
  4. 4.
    Zelnik-Manor, L., Irani, M.: Multi-Frame Estimation of Planar Motion. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 22(10), 1105–1116 (2000)CrossRefGoogle Scholar
  5. 5.
    Irani, M., Rousso, B., Peleg, S.: Recovery of Ego-Motion Using Region Alignment. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 19(3), 268–272 (1997)CrossRefGoogle Scholar
  6. 6.
    Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Intl. Journal of Robotics Research 21(8) (2002)Google Scholar
  7. 7.
    Talukder, A., Matthies, L.: Real-time detection of moving objects from moving vehicles using dense stereo and optical flow. In: IEEE Conference on Intelligent Robots and Systems (IROS), Sendai, Japan (September 2004)Google Scholar
  8. 8.
    Talukder, A., Goldberg, S., Matthies, L., Ansar, A.: Real-time detection of moving objects in a dynamic scene from moving robotic vehicles. In: IEEE Conference on Intelligent Robots and Systems, Las Vegas, NV (October 2003)Google Scholar
  9. 9.
    Black, M., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding (1996)Google Scholar

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.

Personalised recommendations