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A New Tracking Technique: Object Tracking and Identification from Motion

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Book cover Computer Analysis of Images and Patterns (CAIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2756))

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Abstract

Pattern recognition and object tracking play very important roles in various applications, such as motion capture, object detection/recognition, video surveillance, and human computer interface. One very useful method that is rarely mentioned in literature is performing recognition from the motion cue. In many situations, the motion of an object is very representative and informative; therefore, it is possible to identify the object and its behavior from its motion. In this paper, we propose an original method to both identify and track an object in dynamic scenes. The method works on the situations with occlusions, appearance changes and global camera motions. It does not require prior segmentation or initialization. We test this method on a video database containing 18 World Cup soccer videos recorded from TV to detect and track the soccer ball. The results are satisfying. The results are also integrated into a video indexing system and the improvement on video retrieval is described.

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References

  1. Berger, A.L., Della Pietra, S.A., Della Pietra, V.J.: A maximum entropy approach to natural language processing. Computational Linguistics 22 (1996)

    Google Scholar 

  2. Dean, G.C.: An introduction to Kalman filters measurement and control, vol. 19, pp. 69–73

    Google Scholar 

  3. Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach (2002)

    Google Scholar 

  4. Gong, Y., Sin, L.T., Chuan, C.H., Sakauchi, M.: Automatic parsing of TV soccer programs. In: IEEE International Conference on Multimedia Computing and Systems, pp. 167–174 (1995)

    Google Scholar 

  5. Hua, W., Han, M., Gong, Y.: Baseball Scene classification using multimedia features. In: Proc. IEEE Int’l Conf. on Multimedia and Expo (2002)

    Google Scholar 

  6. Isard, M., Blake, A.: CONDENSATION – conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28

    Google Scholar 

  7. Jaimes, A., Chang, S.F.: Automatic selection of visual features and classifiers. In: SPIE Conference on Storage and Retrieval for Media Databases (2000)

    Google Scholar 

  8. Ngo, C.H., Pong, T.C., Zhang, H.J.: On clustering and retrieval of video shots. ACM Press, New York (2001)

    Google Scholar 

  9. Peleg, S., Herman, J.: Panoramic mosaics by manifold projection. In: Computer Vision and Pattern Recognition (CVPR), pp. 338–343 (1997)

    Google Scholar 

  10. Pingali, G., Opalach, A., Jean, Y.: Ball tracking and virtual replays for innovative tennis broadcasts. In: Proceedings of the International Conference on Pattern Recognition

    Google Scholar 

  11. Retz-Schmidt, G.: A replai of soccer: Recognizing intentions in the domain of soccer games. In: Proc. European Conference on Artificial Intelligence, pp. 455–457

    Google Scholar 

  12. Shi, J., Malik, J.: Motion Segmentation and Tracking Using Normalized Cuts. In: International Conference on Computer Vision, ICCV (1998)

    Google Scholar 

  13. Tovinkere, V., Qian, R.J.: Detecting semantic events in soccer games: Towards a complete solution. In: IEEE International Conference on Multimedia and Expo (2001)

    Google Scholar 

  14. Utsumi, O., Miura, K., Ide, I., Sakai, S., Tanaka, H.: An object detection method for describing soccer games from video. In: Proc. IEEE ICME 2002 (2002)

    Google Scholar 

  15. Wu, Y., Huang, T.S.: A Co-inference approach to robust visual tracking. In: Proc. IEEE Int’l Conf. on Computer Vision, Vancouver, Canada, vol. II, pp. 26–33

    Google Scholar 

  16. Xu, P., Xie, L., Chang, S.-F., Divakaran, A., Vetro, A., Sun, H.: Algorithms and systems for segmentation and structure analysis in soccer video. In: ICME 2001 (2001)

    Google Scholar 

  17. Yow, D., Yeo, B.-L., Yeung, M., Liu, B.: Analysis and presentation of soccer highlights from digital video. In: Proc. Asian Conference on Computer Vision

    Google Scholar 

  18. Zhong, D., Chang, S.F.: Structure analysis of sports video using domain models. In: IEEE Conference on Multimedia and Expo, pp. 920–923 (2001)

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Chen, T., Han, M., Hua, W., Gong, Y., Huang, T.S. (2003). A New Tracking Technique: Object Tracking and Identification from Motion. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_20

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  • DOI: https://doi.org/10.1007/978-3-540-45179-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40730-0

  • Online ISBN: 978-3-540-45179-2

  • eBook Packages: Springer Book Archive

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