Context tracker: Exploring supporters and distracters in unconstrained environments

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

Volumn , Issue , 2011, Pages 1177-1184

  Dinh, Thang Ba ^a   Vo, Nam ^b   Medioni, Gérard ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b UNIVERSITY OF NATURAL SCIENCES   (Viet Nam)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDED SYSTEMS; HUMAN COMPUTER INTERACTION; PATTERN RECOGNITION;

APPEARANCE MODELING; CLUTTERED BACKGROUNDS; CONTEXT INFORMATION; MOTION CORRELATION; REAL WORLD VIDEOS; STATE-OF-THE-ART APPROACH; TRACKING FAILURE; UNCONSTRAINED ENVIRONMENTS;

TARGET TRACKING;

EID: 80052910974     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995733     Document Type: Conference Paper

References (30)

1. A. Adam, E. Rivlin, and I. Shimshoni. Robust fragmentsbased tracking using the integral histogram. In CVPR, pages 798-805, 2006.
2. S. Avidan. Support vector tracking. In PAMI, volume 26, pages 1064-1072, 2004.
3. B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. In CVPR, pages 983-990, 2009.
4. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. SURF: Speeded up robust features. In CVIU, volume 110, pages 346-359, 2008.
5. A. Bosch, A. Zisserman, and X. Muoz. Image classification using random forests and ferns. In ICCV, pages 1-8, 2007.
6. L. Breiman. Random forests. In ML, volume 45, pages 5-32, 2001.
7. D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. In PAMI, volume 25, pages 564-577, 2003.
8. T. B. Dinh, N. Vo, and G. Medioni. High resolution face sequences from a PTZ network camera. In FG, 2011.
9. S. K. Divvala, D. Hoiem, J. H. Hays, A. A. Efros, and M. Hebert. An empirical study of context in object detection. In CVPR, pages 1271-1278, 2009.
10. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge 2007 (voc2007) results.
11. J. L. Fan, Y. Wu, and S. Y. Dai. Discriminative spatial attention for robust tracking. In ECCV, pages 480-493, 2010.
12. H. Grabner, C. Leistner, and H. Bishof. Semi-supervised online boosting for robust tracking. In ECCV, pages 234-247, 2008.
13. H. Grabner, J. Matas, L. V. Gool, and P. Cattin. Tracking the invisible: Learning where the object might be. In CVPR, pages 1285-1292, 2010.
14. M. Isard and A. Blake. Condensation - conditional density propagation for visual tracking. In IJCV, volume 29, pages 5-28, 1998.
15. Z. Kalal, J. Matas, and K. Mikolajczyk. Online learning of robust object detectors during unstable tracking. In OLCV, pages 1417-1424, 2009.
16. Z. Kalal, J. Matas, and K. Mikolajczyk. P-N learning: Bootstrapping binary classifiers by structural constraints. In CVPR, pages 49-56, 2010.
17. C.-H. Kuo, C. Huang, and R. Nevatia. Multi-target tracking by on-line learned discriminative appearance models. In CVPR, pages 685-692, 2010.
18. J. S. Kwon and K. M. Lee. Visual tracking decomposition. In CVPR, pages 1269-1276, 2010.
19. A. Li, F. Tang, Y. W. Guo, and H. Tao. Discriminative nonorthogonal binary subspace tracking. In ECCV, pages 258-271, 2010.
20. L. J. Li, R. Socher, and L. F. Fei. Towards total scene understanding: Classification, annotation and segmentation in an automatic framework. In CVPR, pages 2036-2043, 2009.
21. Y. Li, C. Huang, and R. Nevatia. Learning to associate: Hybridboosted multi-target tracker for crowded scene. In CVPR, pages 2953-2960, 2009.
22. X. Mei and H. Ling. Robust visual tracking using L1 minimization. In ICCV, pages 1436-1443, 2009.
23. D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert. Contextual classification with functional max-margin markov network. In CVPR, pages 975-982, 2009.
24. M. Ozuysal, M. Calonder, V. Lepetit, and P. Fua. Fast keypoint recognition using random ferns. In PAMI, volume 32, pages 448-461, 2010.
25. M. Ozuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. In CVPR, pages 1-8, 2007.
26. M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele. What helps where and why? semantic relatedness for knowledge transfer. In CVPR, pages 910-917, 2010.
27. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental learning for robust visual tracking. In IJCV, volume 77, pages 125-141, 2008.
28. M. Yang, Y. Wu, and G. Hua. Context-aware visual tracking. In PAMI, volume 31, pages 1195-1209a, 2009.
29. Q. Yu, T. Dinh, and G. Medioni. Online tracking and reacquisition using co-trained generative and discriminative trackers and discriminative trackers. In ECCV, pages 678-691, 2008.
30. Q. Yu and G. Medioni. Multiple-target tracking by spatiotemporal monte carlo markov chain data association. In PAMI, volume 31, pages 2196-2210, 2009.