Robust tracking using foreground-background texture discrimination

International Journal of Computer Vision

Volumn 69, Issue 3, 2006, Pages 277-293

  Nguyen, Hieu T ^a   Smeulders, Arnold W M ^b  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

Foreground background discrimination; Linear discriminant analysis; Texture; Visual tracking

Indexed keywords

ALGORITHMS; FUNCTIONS; MATHEMATICAL MODELS; NUMERICAL ANALYSIS; ONLINE SYSTEMS;

LINEAR DISCRIMINANT ANALYSIS (LDA); OBJECT DETECTION; ROBUST TRACKING;

TRACKING (POSITION);

EID: 33744916537     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-006-7067-x     Document Type: Article

References (32)

1. Avidan, S. 2004. Support vector tracking. IEEE Trans. on PAMI, 26(8):1064-1072.
2. Black, M.J. and Jepson, A.D. 1996. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. In Proc. of European Conf. on Computer Vision, pp. 329-342.
3. Chen, H.T., Liu, T.L., and Fuh, C.S. 2004. Probabilistic tracking with adaptive feature selection. In Proc. of Int. Conf. on Pattern Recogn. ICPR04, pp. II: 736-739.
4. Chomat, O. and Crowley, J.L. 1999. Probabilistic recognition of activity using local appearance. In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn., pp. II: 104-109.
5. Collins, R. and Liu, Y. 2003. On-line selection of discriminative tracking features. In Proc. IEEE Conf. on Computer Vision, pp. 346-352.
6. Comaniciu, D., Ramesh, V., and Meer, P. 2000. Real-time tracking of non-rigid objects using mean shift. In CVPROO, pp. II: 142-149.
7. Cootes, T.E, Wheeler, G.V., Walker, K.N., and Taylor, C.J. 2002. View-based active appearance models. Image and Vision Computing, 20(9-10):657-664.
8. Daugman, J.G. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. on PAMI, 15(11):1148-1161.
9. Davon, D. 1977. Forest before the trees: The precedence of global features in visual perception. Cognitive Psychology, 9:353-383.
10. Duda, P. O., Hart, P. E., and Stork, D. G. 2001. Pattern Classification. Wiley: New York.
11. Friedman, N. and Russell, S. 1997. Image segmentation in video sequences: A probabilistic approach. In Proc. of 13th Conf. on Uncertainty in Artificial Intelligence, pp. 175-181.
12. Gong, S., McKenna, S.J., and Collins J.J. 1996. An investigation into face pose distributions. In Proc. of 2nd Inter. Conf. on Automated Face and Gesture Recognition, pp. 265. Killington, Vermont.
13. Hayman, E. and Eklundh, J.O. 2003. Statistical background subtraction for a mobile observer. In Proc. IEEE Conf. on Computer Vision, pp. 67-74.
14. Isard, M., and MacCormick, J.P. 2001. BraMBLe: A Bayesian multiple-blob tracker. In Proc. IEEE Conf. on Computer Vision, pp. 11:34-41.
15. Jain, A.K. and Farrokhnia, F. 1991. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 24(12): 1167-1186.
16. Jepson, A.D., Fleet, D.J., and El-Maraghi, T.F. 2001. Robust online appearance models for visual tracking. In Proc. IEEE Conf. on Computer Vision and Pattern Recogn., CVPR01, pp. I: 415-422.
17. Jojic, N., Frey, B.J., and Kannan, A. 2003. Epitomic analysis of appearance and shape. In Proc. IEEE Conf. on Computer Vision, pp. 34-41.
18. Lucas, B.D. and Kanade, T. 1981. An iterative image registration technquie with an application to stereo vision. In Proc. DARPA Imaging Understanding Workshop, pp. 121-130.
19. Matthews, I., Ishikawa, T., and Baker, S. 2004. The template update problem. IEEE Trans. on PAMI, 26(6):810-815.
20. Nguyen, H.T. and Smeulders, A.W.M. 2004. Fast occluded object tracking by a robust appearance filter. IEEE Trans. on PAMI, 26(8):1099-1104.
21. Nguyen, H.T. and Smeulders, A.W.M. 2004. Tracking aspects of the foreground against the background. In Proc. of European Conf. on Computer Vision, pp. Vol II: 446-456.
22. Ravela, S., Draper, B.A., Lim, J., and Weiss, R. 1996. Tracking object motion across aspect changes for augmented reality. In ARPA Image Understanding Workshop, pp. 1345-1352.
23. Rittscher, J., Kato, J., Joga, S., and Blake, A. 2000. A probabilistic background model for tracking. In Proc. of European Conf. on Computer Vision, pp. II: 336-350.
24. Ross, D., Lim, J., and Yang, M.H. 2004. Adaptive probabilistic visual tracking with incremental subspace update. In Proc. of European Conf. on Computer Vision, pp. Vol II: 470-482.
25. Roth, S., Sigal, L., and Black, M.J. 2004. Gibbs likelihoods for bayesian tracking. In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn., pp. I: 886-893.
26. Sidenbladh, H. and Black, M.J. 2003. Learning the statistics of people in images and video. Int. J. Computer Vision, 54(1):181-207.
27. Stauffer, C. and Grimson, W.E.L. 1999. Adaptive background mixture models for real-time tracking. In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn., pp. II: 246-252.
28. Sullivan, J., Blake, A., and Rittscher, J. 2000. Statistical foreground modelling for object localisation. In Proc. of European Conf. on Computer Vision, pp. II: 307-323.
29. Tao, H., Sawhney, H.S., and Kumar, R. 2000. Dynamic layer representation with applications to tracking. In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn., CVPR00, pp. 11:134-141.
30. Torralba, A. and Sinha, P. 2001. Statistical context priming for object detection. In Proc. IEEE Conf. on Computer Vision.
31. Wren, C.R., Azarbayejani, A., Darrell, T.J., and Pentland, A.P. 1997. Pfinder: Real-time tracking of the human body. IEEE Trans. on PAMI, 19(7):780-785.
32. Wu, Y. and Huang, Th. S. 2000. Color tracking by transductive learning. In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn., pp. I:133-138, 2000.