MEEM: Robust tracking via multiple experts using entropy minimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8694 LNCS, Issue PART 6, 2014, Pages 188-203

  Zhang, Jianming ^a   Ma, Shugao ^a   Sclaroff, Stan ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; BUDGET CONTROL; ENTROPY; EXPERIMENTS; INFERENCE ENGINES;

BENCHMARK DATASETS; ENTROPY CRITERIA; ENTROPY MINIMIZATION; FEATURE MAPPING; ON-LINE TRACKING; ROBUST TRACKING; TRACKING METHOD; VIDEO SEQUENCES;

RESTORATION;

EID: 84906331169     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10599-4_13     Document Type: Conference Paper

References (32)

1. Avidan, S.: Support vector tracking. PAMI 26(8), 1064-1072 (2004)
2. Avidan, S.: Ensemble tracking. PAMI 29(2), 261-271 (2007)
3. Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. PAMI 33(8), 1619-1632 (2011)
4. Bai, Q., Wu, Z., Sclaroff, S., Betke, M., Monnier, C.: Randomized ensemble tracking. In: ICCV (2013)
5. Chu, D.M., Smeulders, A.W.: Thirteen hard cases in visual tracking. In: AVSS (2010)
6. Dinh, T.B., Vo, N., Medioni, G.: Context tracker: Exploring supporters and distracters in unconstrained environments. In: CVPR (2011)
7. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)
8. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234-247. Springer, Heidelberg (2008)
9. Grabner, M., Grabner, H., Bischof, H.: Learning features for tracking. In: CVPR (2007)
10. Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NIPS (2005)
11. Hare, S., Saffari, A., Torr, P.H.: Struck: Structured output tracking with kernels. In: ICCV (2011)
12. He, S., Yang, Q., Lau, R.W., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: CVPR (2013)
13. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)
14. Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: Bootstrapping binary classifiers by structural constraints. In: CVPR (2010)
15. Kim, M., Kumar, S., Pavlovic, V., Rowley, H.: Face tracking and recognition with visual constraints in real-world videos. In: CVPR (2008)
16. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)
17. Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV (2011)
18. Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., van den Hengel, A.: A survey of appearance models in visual object tracking. arXiv preprint arXiv:1303.4803 (2013)
19. Maji, S., Berg, A.C.: Max-margin additive classifiers for detection. In: CVPR (2009)
20. Matthews, L., Ishikawa, T., Baker, S.: The template update problem. PAMI 26(6), 810-815 (2004)
21. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. PAMI 33(11), 2259-2272 (2011)
22. Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient l1 tracker with occlusion detection. In: CVPR (2011)
23. Supancic III, J.S., Ramanan, D.: Self-paced learning for long-term tracking. In: CVPR (2013)
24. Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. In: ICCV (2007)
25. Wang, D., Lu, H., Yang, M.H.: Least soft-thresold squares tracking. In: CVPR (2013)
26. Wang, Z., Vucetic, S.: Online training on a budget of support vector machines using twin prototypes. Statistical Analysis and Data Mining 3(3), 149-169 (2010)
27. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: A benchmark. In: CVPR (2013)
28. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys (CSUR) 38(4), 13 (2006)
29. Yu, Q., Dinh, T.B., Medioni, G.: Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 678-691. Springer, Heidelberg (2008)
30. Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, Springer, Heidelberg (1994)
31. Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Low-rank sparse learning for robust visual tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 470-484. Springer, Heidelberg (2012)
32. Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: CVPR (2012)