Learning features for tracking

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Grabner, Michael ^a   Grabner, Helmut ^a   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

REAL TIME SYSTEMS; TARGET TRACKING;

LEARNING FEATURES; ON-LINE BOOSTING;

FEATURE EXTRACTION;

EID: 34948846147     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.382995     Document Type: Conference Paper

References (28)

1. S. Avidan. Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:1064-1072, 2004.
2. S. Avidan. Ensemble tracking. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 494-501, 2005.
3. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transaction on Pattern Analysis Machine Intelligence, 24(4):509-522, 2002.
4. M. J. Black and A. D. Jepson. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision, 26(1):63-84, 1998.
5. R. Collins, Y. Liu, and M. Leordeanu. Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10):1631-1643, 2005.
6. T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
7. P. Gabriel, J.-B. Hayet, J. Piater, and J. Verly. Object tracking using color interest points. In IEEE Conference on Advanced Video and Signal Based Surveillance, pages 159-164, 2005.
8. H. Grabner and H. Bischof. On-line boosting and vision. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 260-267, 2006.
9. H. Grabner, M. Grabner, and H. Bischof. Real-time tracking via on-line boosting. In Proceedings 17th British Machine Vision Conference, volume 1, pages 47-56, 2006.
10. G. D. Hager and P. N. Belhumeur. Efficient region tracking with parametric models of geometry and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(10):1025-1039, 1998.
11. C. Harris and M. Stephens. A combined corner and edge detection. In Proceedings of The Fourth Alvey Vision Conference, pages 147-151, 1988.
12. R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, second edition, 2004.
13. T. Hertz, A. Bar-Hillel, and D. Weinshall. Boosting margin based distance functions for clustering. In International Conference on Machine Learning, page 50, 2004.
14. A. D. Jepson, D. J. Fleet, and T. El-Maraghi. Robust online appearance models for visual tracking. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 415 - 422, 2001.
15. V. Lepetit, P. Lagger, and P. Fua. Randomized trees for real-time keypoint recognition. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 775 - 781, 2005.
16. V. Lepetit, J. Pilet, and P. Fua. Point matching as a classification problem for fast and robust object pose estimation. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 244-250, 2004.
17. J. Lim, D. Ross, R. Lin, and M. Yang. Incremental learning for visual tracking. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Neural Information Processing Systems, number 17, pages 793-800. 2005.
18. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
19. L. Masson, M. Dhome, and F. Jurie. Robust real time tracking of 3d objects. In Proceedings of 17th International Conference on Pattern Recognition, pages 252-255, 2004.
20. J. Meltzer, M.-H. Yang, R. Gupta, and S. Soatto. Multiple view feature descriptors from image sequences via kernel principal component analysis. In Proceedings 8th European Conference on Computer Vision, pages 215-227, 2004.
21. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10):1615-1630, 2005.
22. H. Nguyen and A. Smeulders. Tracking aspects of the foreground against the background. In Proceedings 8th European Conference on Computer Vision, volume 2, pages 446 - 456, 2004.
23. M. Özuysal, V. Lepetit, F. Fleuret, and P. Fua. Feature harvesting for tracking-by-detection. In Proceedings 10th European Conference on Computer Vision, volume 3, pages 592-605, 2006.
24. R. Schapire, Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proceedings International Conference on Machine Learning, pages 322-330, 1997.
25. J. Shi and C. Tomasi. Good features to track. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pages 593 - 600, 1994.
26. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 511-518, 2001.
27. J. Wang, X. Chen, and W. Gao. Online selecting discriminative tracking features using particle filter. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 1037-1042, 2005.
28. O. Williams, A. Blake, and R. Cipolla. Sparse bayesian learning for efficient visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8):1292-1304, 2005.