Kernel-Bayesian framework for object tracking

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

Volumn 4843 LNCS, Issue PART 1, 2007, Pages 821-831

  Zhang, Xiaoqin ^a   Hu, Weiming ^a   Luo, Guan ^a   Maybank, Steve ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CONSTRAINT THEORY; HEURISTIC ALGORITHMS; TRACKING (POSITION);

BAYESIAN TRACKERS; TRACKING FRAMEWORKS;

OBJECT RECOGNITION;

EID: 38149140908     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-76386-4_78     Document Type: Conference Paper

References (14)

1. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. International Journal of Computer Vision 1(4), 321-332 (1988)
2. Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5-28 (1998)
3. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 234-240 (2003)
4. Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1296-1311 (2003)
5. Rasmussen, C., Hager, G.D.: Probabilistic Data Association Methods for Tracking Complex Visual Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 560-576 (2001)
6. Hager, G.D., Hager, P.N.: Efficient region tracking with parametric models of geometry andillumination. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(10), 1025-1039 (1998)
7. Sullivan, J., Rittscher, J.: Guiding random particles by deterministic search. In: International Conference on Computer Vision, pp. 323-330 (2001)
8. Zhou, S., Chellappa, R., Moghaddam, B.: Visual Tracking and Recongnition Using Appearance-adaptive Models in Particles Filters. IEEE Transaction on Image Processing 13(11), 1491-1506 (2004)
9. McKenna, S., Jabri, S., Gong, S.: Tracking Colour Objects Using Adaptive Mixture Models. Image and Vision Computing 17(3-4), 225-231 (1999)
10. Fukunaga, K., Hostetler, L.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory 21(1), 32-40 (1975)
11. Aralampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particles filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174-188 (2002)
12. Yang, C.J., Duraiswami, R., Davis, L.: Fast multiple object tracking via a hierarchical particle filter. In: Proceeding of International Conference on Computer Vision, pp. 212-219 (2005)
13. Nummiaro, K., Koller-Meierand, E., Van Gool, L.: An Adaptive Color-Based Particle Filter. Image and Vision Computing 21(1), 99-110 (2003)
14. Parameswaran, V., Ramesh, V., Zoghlami, I.: Tunable Kernels for Tracking. In: Proceeding of International Conference on Computer Vision and Pattern Recognition, pp. 2179-2186 (2006)