Robust auxiliary particle filter with an adaptive appearance model for visual tracking

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

Volumn 6494 LNCS, Issue PART 3, 2011, Pages 718-731

  Kim, Du Yong ^a   Yang, Ehwa ^a   Jeon, Moongu ^a   Shin, Vladimir ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE APPEARANCE MODELS; AUXILIARY PARTICLE FILTER; BEST FIT; COMPUTATIONAL LOADS; DYNAMIC MOTION MODELS; HIGH-DIMENSIONAL; MOTION MODELS; OBJECT APPEARANCE; PARTICLE FILTERING; PIECEWISE LINEAR; SAMPLING STRATEGIES; SLIDING WINDOW; STATE SPACE; VISUAL TRACKING;

ALGORITHMS; NONLINEAR FILTERING; PIECEWISE LINEAR TECHNIQUES; PRINCIPAL COMPONENT ANALYSIS; UNCERTAINTY ANALYSIS;

COMPUTER VISION;

EID: 79952520066     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19318-7_56     Document Type: Conference Paper

References (24)

1. Kwon, J., Park, F.C.: Visual tracking via particle filtering on the affine group. IJRR (2009)
2. Kwon, J., Lee, K.M., Park, F.C.: Visual tracking via geometric particle filtering on the affine group with optimal importance functions. In: CVPR (2009)
3. Barbenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)
4. Li, Y., Ai, H., Yamashida, T., Lao, S., Kawade, M.: Tracking in low fame rate video: A cascade particle filter with discriminative observers of different life spans. IEEE PAMI, 1728-1740 (2008)
5. Matthews, I., Ishikawa, T., Baker, S.: The template update problem. IEEE PAMI, 810-815 (2004)
6. Yang, M., Fan, Z., Wu, Y.: Tracking nonstationary visual appearances by datadriven adaptation. IEEE TIP, 1633-1644 (2009)
7. Han, B.H., Zhu, Y., Comaniciu, D., Davis, S.L.: Kernel-based bayesian filtering for object tracking. In: CVPR (2005)
8. Ross, R.D., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. IJCV (2008)
9. Jepson, A.D., Fleet, J., Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE PAMI (2001)
10. Jazwinski, A.: Stochastic process and filtering theory. Academic Press, London (1970)
11. Kwon, W.H., Kim, P.S., Park, P.G.: A receding horizon kalman fir filter for linear continuous-time systems. IEEE TAC (1999)
12. Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: CVPR (2003)
13. Lim, H., Morariu, V., Camps, O.I., Sznaier, M.: Dynamic appearance modeling for human tracking. In: CVPR (2006)
14. Blake, A., Isard, M.: Active contours. Springer, Heidelberg (1998)
15. Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065. Springer, Heidelberg (1996)
16. Rui, Y., Chen, Y.: Online learning of probabilistic appearance manifolds for videobased recognition and tracking. In: CVPR (2001)
17. Doucet, A., Goodsil, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 197-208 (2000)
18. Pitt, M., Shephard, N.: Filtering via simulation: auxiliary particle filters. Journal of American Statistical Association, 590-599 (1999)
19. Vlassis, N., Terwijin, B., Kros, B.: Auxiliary particle filter robot localization from high-dimensional sensor observations. In: ICRA (2002)
20. Deutsher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filter. In: CVPR (2000)
21. Shen, C., Kim, J.,Wang, H.: Generalized kernel-based visual tracking. IEEE CSVT, 119-130 (2010)
22. Levy, A., Lindenbaum, M.: Sequential karhunen-loeve basis extraction and its application to images. IEEE TIP, 1371-1374 (2000)
23. Lee, K.C., Kriegman, D.: Online learning of probabilistic appearance manifolds for video-based recognition and tracking. In: CVPR (2005)
24. Kim, M., Kumar, S., Pavlovic, V., Rowley, H.: Face tracking and recognition with visual constraints in real-world videos. In: CVPR (2008)