Learning adaptive metric for robust visual tracking

IEEE Transactions on Image Processing

Volumn 20, Issue 8, 2011, Pages 2288-2300

  Jiang, Nan ^a   Liu, Wenyu ^a   Wu, Ying ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Northwestern University   (United States)

Author keywords

Adaptive; discriminative; metric learning; supervised; visual tracking

Indexed keywords

ADAPTIVE; DISCRIMINATIVE; METRIC LEARNING; SUPERVISED; VISUAL TRACKING;

LEARNING SYSTEMS; MOTION ESTIMATION; VISUALIZATION;

TARGET TRACKING;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; HUMAN; IMAGE PROCESSING; METHODOLOGY; VIDEORECORDING;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; VIDEO RECORDING;

EID: 79960532056     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2011.2114895     Document Type: Article

References (28)

1. G. D. Hager, M. Dewan, and C. V. Stewart, "Multiple kernel tracking with ssd," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2004, pp. I-790-I-797.
2. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564-577, May 2003.
3. A. Elgammal, R. Duraiswami, and L. S. Davis, "Probabilistic tracking in joint feature-spatial spaces," in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recog., 2003, pp. I-781-I-788.
4. P. Viola and W. M. Wells, "Alignment by maximization of mutual information," in Proc. IEEE Int. Conf. Comput. Vision, 1995, p. 16.
5. C. Yang, R. Duraiswami, and L. Davis, "Efficient mean-shift tracking via a new similarity measure," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2005, pp. 176-183.
6. R. T. Collins, Y. Liu, and M. Leordeanu, "Online selection of discriminative tracking features," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1631-1643, Oct. 2005. (Pubitemid 41555407)
7. H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting," 2006.
8. B. Han and L. Davis, "Object tracking by adaptive feature extraction," in Proc. Int. Conf. Image Process., 2004, pp. 1501-1504. (Pubitemid 40820508)
9. Z. Yin, F. Porikli, and R. T. Collins, "Likelihood map fusion for visual object tracking," in Proc. IEEE Workshop Appl. Comput. Vision, 2008, pp. 1-7.
10. S. Avidan, "Support vector tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 1064-1072, Aug. 2004.
11. S. Avidan, "Ensemble tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 2, pp. 261-271, Feb. 2007. (Pubitemid 46420886)
12. B. Babenko, M.-H. Yang, and S. Belongie, "Visual tracking with online multiple instance learning," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2009, pp. 983-990.
13. M. J. Black and A. D. Jepson, "Eigentracking: Robust matching and tracking of articulated objects using a view-based representation," Int. J. Comput. Vision, vol. 26, no. 1, pp. 63-84, 1998. (Pubitemid 128513060)
14. T. F. Cox and M. A. A. Cox, Multidimensional Scaling, har/dis ed. London, U.K.: Chapman & Hall, Jan. 1994.
15. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, pp. 2323-2326, 2000. (Pubitemid 32041578)
16. J. B. Tenenbaum, V. Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, pp. 2319-2323, Dec. 2000. (Pubitemid 32041577)
17. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, "Distance metric learning, with application to clustering with side-information," in Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press, 2002, pp. 505-512.
18. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, "Learning distance functions using equivalence relations," in Proc. Int. Conf. Mach. Learn., 2003, pp. 11-18.
19. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, "Neighborhood component analysis," Adv. Neural Inf. Process. Syst., vol. 17, pp. 513-520, 2005.
20. K. Weinberger, J. Blitzer, and L. Saul, "Distance metric learning for large margin nearest neighbor classification," presented at the Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2006.
21. X. Wang, G. Hua, and T. X. Han, "Discriminative tracking by metric learning," presented at the 11th Conf. Eur. Conf. Comput. Vision, Crete, Greece, 2010.
22. A. Globerson and S. Roweis, "Metric learning by collapsing classes," Adv. Neural Inf. Process. Syst., vol. 18, pp. 451-458, 2006.
23. D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of nonrigid objects using mean shift," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2000, pp. 142-149.
24. Y. Wu and J. Fan, "Contextual flow," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2009, pp. 33-40.
25. R. T. Collins, "Mean-shift blob tracking through scale space," in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recog., 2003, pp. II-234-II-240.
26. C. Shen, J. Kim, and H. Wang, "Generalized kernel-based visual tracking," IEEE Trans. Circuits Syst. for Video Technol., vol. 20, no. 1, pp. 119-130, Jan. 2010.
27. A. Yilmaz, "Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2007, pp. 1-6.
28. Z. Fan, M. Yang, Y. Wu, G. Hua, and T. Yu, "Efficient optimal kernel placement for reliable visual tracking," in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recog., 2006, pp. 658-665. (Pubitemid 44931388)