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Semi-automatic object tracking in video sequences
Keywords:
signal processing, video, segmentation, objects, recognitionAbstract
A method is presented for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modelling the a posteriori probabilities of a set of features such as color, position and motion, depth, etc.
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References
[1] A. F. Bobick and S. S. Intille. Large Occlusion Stereo. Int. Journal of Computer Vi-
[2] R. Castagno, T. Ebrahimi, and M. Kunt. Video Segmentation Based on Multiple Features for Interactive Multimedia Applications. IEEE Trans. on Circuits and Systems for Video Tech., 8(5):562–571, September 1998.
[3] M. Everingham and B. Thomas. Supervised Segmentation adn Tracking of Nonrigid Objects using a Mixture of Histograms Model. In ICIP01 - Int. Conf. on Image Processing, pages 62–65, 2001.
[4] M. Figueiredo and A. Jain. Unsupervised Learning of Finite Mixture Models. IEEE Trans. on Pattern and Machine Intelligence, 24(3):381–396, March 2002.
[5] H. Greenspan, J. Goldberger, and A. Meyer. Probabilistic Space-Time Video Modeling via Piecewise GMM. IEEE Tran. on Pattern Analysis and Machine Intelligence, 26(3):384–396, March 2004.
[6] M. Harville, G. Gordon, and J. Woodfill. Foreground segmentation using adaptive mixture models in color and depth. In IEEE Workshop on detection and recognition of events in video, pages 3–11, 2001.
[7] E. Hayman and J. Eklundh. Probabilistic and Voting Approaches to Cue Integration for Figure-Ground Segmentation. In ECCV 2002, LNCS 2352, pages 469–486.
[8] S. Khan and M. Shah. Object Based Segmentation of Video using Color, Motion and Saptial Information. In CVPR2001 - Int. Conf. Computer Vision and Pattern Recognition, volume 2, pages 746–751, 2001.
[9] N. Friedman and S. Rusell. Image segmentation in video sequence: A probabilistic approach. In Conf. Uncertainty in Artificial Intelligence, number 13, 1997.
[10] A. Pardo and G. Sapiro. Vector Probability Diffusion. IEEE Signal Processing Letters, 8(4):106–109, April 2001.
[11] M. Spengler and B. Schiele. Towards robust multi-cue integration for visual tracking. Machine Vision and Applications, 14:50–58, 2003.
[12] D. Tax, M. van Breukelen, R. Duin, and J. Kittler. Combining multiple classifiers by averaging or by multiplying? Pattern Recognition, 33:1475–1485, 2000.
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[14] J. Wang and E. Adelson. Representing moving images with layers. IEEE Trans. on Image Processing, 3(5):625–638, September 1994.
[2] R. Castagno, T. Ebrahimi, and M. Kunt. Video Segmentation Based on Multiple Features for Interactive Multimedia Applications. IEEE Trans. on Circuits and Systems for Video Tech., 8(5):562–571, September 1998.
[3] M. Everingham and B. Thomas. Supervised Segmentation adn Tracking of Nonrigid Objects using a Mixture of Histograms Model. In ICIP01 - Int. Conf. on Image Processing, pages 62–65, 2001.
[4] M. Figueiredo and A. Jain. Unsupervised Learning of Finite Mixture Models. IEEE Trans. on Pattern and Machine Intelligence, 24(3):381–396, March 2002.
[5] H. Greenspan, J. Goldberger, and A. Meyer. Probabilistic Space-Time Video Modeling via Piecewise GMM. IEEE Tran. on Pattern Analysis and Machine Intelligence, 26(3):384–396, March 2004.
[6] M. Harville, G. Gordon, and J. Woodfill. Foreground segmentation using adaptive mixture models in color and depth. In IEEE Workshop on detection and recognition of events in video, pages 3–11, 2001.
[7] E. Hayman and J. Eklundh. Probabilistic and Voting Approaches to Cue Integration for Figure-Ground Segmentation. In ECCV 2002, LNCS 2352, pages 469–486.
[8] S. Khan and M. Shah. Object Based Segmentation of Video using Color, Motion and Saptial Information. In CVPR2001 - Int. Conf. Computer Vision and Pattern Recognition, volume 2, pages 746–751, 2001.
[9] N. Friedman and S. Rusell. Image segmentation in video sequence: A probabilistic approach. In Conf. Uncertainty in Artificial Intelligence, number 13, 1997.
[10] A. Pardo and G. Sapiro. Vector Probability Diffusion. IEEE Signal Processing Letters, 8(4):106–109, April 2001.
[11] M. Spengler and B. Schiele. Towards robust multi-cue integration for visual tracking. Machine Vision and Applications, 14:50–58, 2003.
[12] D. Tax, M. van Breukelen, R. Duin, and J. Kittler. Combining multiple classifiers by averaging or by multiplying? Pattern Recognition, 33:1475–1485, 2000.
13] D. Thirde, G. Jones, and J. Flack. SpatioTemporal Semantic Object Segmentation using Probabilistic Sub-Object Regions. In BMVC2003, 2003.
[14] J. Wang and E. Adelson. Representing moving images with layers. IEEE Trans. on Image Processing, 3(5):625–638, September 1994.
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Published
2005-12-01
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[1]
“Semi-automatic object tracking in video sequences”, JCS&T, vol. 5, no. 04, pp. p. 218–224, Dec. 2005, Accessed: Mar. 12, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/839
