Open Access

A Feedback-Based Algorithm for Motion Analysis with Application to Object Tracking

EURASIP Journal on Advances in Signal Processing20072007:086064

https://doi.org/10.1155/2007/86064

Received: 1 December 2005

Accepted: 14 October 2006

Published: 28 January 2007

Abstract

We present a motion detection algorithm which detects direction of motion at sufficient number of points and thus segregates the edge image into clusters of coherently moving points. Unlike most algorithms for motion analysis, we do not estimate magnitude of velocity vectors or obtain dense motion maps. The motivation is that motion direction information at a number of points seems to be sufficient to evoke perception of motion and hence should be useful in many image processing tasks requiring motion analysis. The algorithm essentially updates the motion at previous time using the current image frame as input in a dynamic fashion. One of the novel features of the algorithm is the use of some feedback mechanism for evidence segregation. This kind of motion analysis can identify regions in the image that are moving together coherently, and such information could be sufficient for many applications that utilize motion such as segmentation, compression, and tracking. We present an algorithm for tracking objects using our motion information to demonstrate the potential of this motion detection algorithm.

[123456789101112131415161718192021222324252627282930]

Authors’ Affiliations

(1)
Department of Electrical Engineering, Indian Institute of Science

References

  1. Aggarwal JK, Nandhakumar N: On the computation of motion from sequences of images: a review. Proceedings of the IEEE 1988,76(8):917-935. 10.1109/5.5965View ArticleGoogle Scholar
  2. Stiller C, Konrad J: Estimating motion in image sequences, a tutorial on modeling and computation of 2d motion. IEEE Signal Processing Magazine 1999,16(4):70-91. 10.1109/79.774934View ArticleGoogle Scholar
  3. Horn BKP, Schunck BG: Determining optic flow. Artificial Intelligence 1981,17(1–3):185-203.View ArticleGoogle Scholar
  4. Bruhn A, Weickert J, Schnörr C: Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. International Journal of Computer Vision 2005,61(3):211-231.View ArticleGoogle Scholar
  5. Radke RJ, Andra S, Al-Kofahi O, Roysam B: Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing 2005,14(3):294-307.MathSciNetView ArticleGoogle Scholar
  6. Casile A, Giese MA: Critical features for the recognition of biological motion. Journal of Vision 2005,5(4):348-360.View ArticleGoogle Scholar
  7. Shah S: Feedback in low level vision: computional models and applications, Ph.D. thesis. Indian Institute of Science, Bangalore, India; 2001.Google Scholar
  8. Mumford D: Thalamus. In The Handbook of Brain Theory and Neural Networks. Edited by: Arbib MA. MIT Press, Cambridge, Mass, USA; 1995:153-157.Google Scholar
  9. Sastry PS, Shah S, Singh S, Unnikrishnan KP: Role of feedback in mammalian vision: a new hypothesis and a computational model. Vision Research 1999,39(1):131-148. 10.1016/S0042-6989(98)00085-6View ArticleGoogle Scholar
  10. Shah S, Sastry PS, Unnikrishnan KP: A feedback based algorithm for line detection. Proceedings of Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP '98), December 1998, New Delhi, IndiaGoogle Scholar
  11. Shah S, Sastry PS: Finger print classification using a feedback-based line detector. IEEE Transactions on Systems, Man, and Cybernetics, Part B 2004,34(1):85-94. 10.1109/TSMCB.2002.806486View ArticleGoogle Scholar
  12. Adelson EH, Movshon JA: Phenomenal coherence of moving visual patterns. Nature 1982,300(5892):523-525. 10.1038/300523a0View ArticleGoogle Scholar
  13. Livingstone MS: Mechanisms of direction selectivity in macaque v1. Neuron 1998,20(3):509-526. 10.1016/S0896-6273(00)80991-5View ArticleGoogle Scholar
  14. Merabet L, Desautels A, Minville K, Casanova C: Motion integration in a thalamic visual nucleus. Nature 1998,396(6708):265-268. 10.1038/24382View ArticleGoogle Scholar
  15. Hu W, Tan T, Wang L, Maybank SJ: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man and Cybernetics, Part C 2004,34(3):334-352. 10.1109/TSMCC.2004.829274View ArticleGoogle Scholar
  16. Tekalp A: Digital Video Processing. Prentice-Hall PTR, Englewood Cliffs, NJ, USA; 1995.Google Scholar
  17. Shah S, Sastry PS: Object tracking using motion direction detection. Proceedings of Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP '00), December 2000, Bangalore, IndiaGoogle Scholar
  18. Pallbo R: Mind in motion, Ph.D. thesis. Lund University Cognitive Studies, Lund, Sweden; 1997.Google Scholar
  19. Dickinson SJ, Jasiobedzki P, Olofsson G, Christensen HI: Qualitative tracking of 3-d objects using active contour networks. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '94), June 1994, Seattle, Wash, USA 812-817.Google Scholar
  20. Caselles V, Kimmel R, Sapiro G: Geodesic active contours. Proceedings of the 5th International Conference on Computer Vision (ICCV '95), June 1995, Cambridge, Mass, USA 694-699.View ArticleGoogle Scholar
  21. Kichenassamy S, Kumar A, Olver P, Tannenbaum A, Yezzi A: Gradient flows and geometric active contour models. Proceedings of the 5th International Conference on Computer Vision (ICCV '95), June 1995, Cambridge, Mass, USA 810-815.View ArticleGoogle Scholar
  22. Heisele B, Kressel U, Ritter W: Tracking non-rigid, moving objects based on color cluster flow. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 257-260.View ArticleGoogle Scholar
  23. Paragios N, Deriche R: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(3):266-280. 10.1109/34.841758View ArticleGoogle Scholar
  24. Kim M, Jeon JG, Kwak JS, Lee MH, Ahn C: Moving object segmentation in video sequences by user interaction and automatic object tracking. Image and Vision Computing 2001,19(5):245-260. 10.1016/S0262-8856(00)00074-3View ArticleGoogle Scholar
  25. Isard M, Blake A: Condensation—conditional density propagation for visual tracking. International Journal of Computer Vision 1998,29(1):5-28. 10.1023/A:1008078328650View ArticleGoogle Scholar
  26. MacCormick J, Blake A: A probabilistic exclusion principle for tracking multiple objects. International Journal of Computer Vision 2000,39(1):57-71. 10.1023/A:1008122218374View ArticleMATHGoogle Scholar
  27. Hue C, Le Cadre J-P, Perez P: A particle filter to track multiple objects. Proceedings of IEEE Workshop on Multi-Object Tracking, July 2001, Vancouver, BC, Canada 61-68.View ArticleGoogle Scholar
  28. Gavrila DM, Davis LS: 3-d model-based tracking of humans in action: a multi-view approach. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '96), June 1996, San Francisco, Calif, USA 73-80.View ArticleGoogle Scholar
  29. Bregler C: Learning and recognizing human dynamics in video sequences. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 568-574.View ArticleGoogle Scholar
  30. Huttenlocher DP, Rucklidge WJ: A multi-resolution technique for comparing images using the hausdorff distance. In Tech. Rep. TR 92-1321. Department of Computer science, Cornell University, Ithaca, NY, USA; 1992.Google Scholar

Copyright

© Shah and Sastry 2007