Visual tracking via adaptive tracker selection with multiple features

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

Volumn 7575 LNCS, Issue PART 4, 2012, Pages 28-41

  Yoon, Ju Hong ^a   Kim, Du Yong ^a   Yoon, Kuk Jin ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

Author keywords

appearance learning; multiple features; robust likelihood function; tracker interaction; transition probability matrix; Visual tracking

Indexed keywords

APPEARANCE LEARNING; LIKELIHOOD FUNCTIONS; MULTIPLE FEATURES; TRACKER INTERACTION; TRANSITION PROBABILITY MATRIX; VISUAL TRACKING;

PROBABILITY; RECURSIVE FUNCTIONS; TRACKING (POSITION);

COMPUTER VISION;

EID: 84867844907     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33765-9_3     Document Type: Conference Paper

References (28)

1. Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. IJCV 29(1), 5-28 (1998)
2. Jilkov, V.P., Li, X.R.: Online Bayesian estimation of transition probabilities for markovian jump systems. IEEE Transactions on Signal Processing 52(6), 307-315 (2004)
3. Ross, D., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. IJCV 77, 125-141 (2008)
4. Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: CVPR, pp. 723-730 (2010)
5. Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Machine Vision and Applications 14(1), 50-58 (2003)
6. Giebel, J., Gavrila, D.M., Schnörr, C.: A Bayesian Framework for Multi-cue 3D Object Tracking. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 241-252. Springer, Heidelberg (2004)
7. Brasnett, P., Mihaylova, L., Canagarajah, N., Mihaylova, L., Canagarajah, N., Bull, D.: Particle filtering with multiple cues For object tracking. In: Proc. of SPIE's Annual Symp. EI ST, pp. 430-441 (2005)
8. Wang, H., Suter, D.: Efficient visual tracking by probabilistic fusion of multiple cues. In: International Conference on Pattern Recognition, pp. 892-895 (2006)
9. Leichter, I., Lindenbaum, M., Rivlin, E.: A general framework for combining visual trackers - the "black boxes" approach. IJCV 67(3), 343-363 (2006)
10. Badrinarayanan, V., Perez, P., Clerc, F.L., Oisel, L.: Probabilistic color and adaptive multi-feature tracking with dynamically switched priority between cues. In: ICCV, pp. 1-8 (2007)
11. Du,W., Piater, J.: A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 225-238. Springer, Heidelberg (2008)
12. Moreno-Noguer, F., Sanfeliu, A., Samaras, D.: Dependent multiple cue integration for robust tracking. PAMI 30(4), 670-685 (2008)
13. Stenger, B., Woodley, T., Cipolla, R.: Learning to track with multiple observers. In: CVPR, pp. 2647-2654 (2009)
14. Zelniker, E.E., Hospedales, T.M., Gong, S., Xiang, T.: A unified Bayesian framework for adaptive visual tracking. In: BMVC, pp. 18.1-18.11 (2009)
15. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR, pp. 1269-1276 (2010)
16. Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV, pp. 1436-1443 (2009)
17. Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with applications to tracking and navigation. Wiley, New York (2001)
18. Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient l1 tracker with occlusion detection. In: CVPR, pp. 1257-1264 (2011)
19. Kim, S.-J., Koh, K., Lustig,M., Boyd, S., Gorinevsky, D.: An interior-point method for largescale l1 regularized least squares. IEEE Journal on Selected Topics in Signal Processing 1(4), 606-617 (2007)
20. Yang, M.-H.: Face detection. In: Encyclopedia of Biometrics, pp. 303-308 (2009)
21. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886-893 (2005)
22. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Computing Surveys 38(4) (2006)
23. Cifuentes, C.G., Sturzel, M., Jurie, F., Brostow, G.J.: Motion models that only work sometimes. In: BMVC (2012)
24. Everingham, M., Van Gool, L.J., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88(2), 303-338 (2010)
25. Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197-208 (2000)
26. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, pp. 798-805 (2006)
27. Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR, pp. 983-990 (2009)
28. Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: bootstrapping binary classifiers by structural constraints. In: CVPR, pp. 49-56 (2010)