Robust scale-adaptive mean-shift for tracking

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

Volumn 7944 LNCS, Issue , 2013, Pages 652-663

  Vojir, Tomas ^a   Noskova, Jana ^a   Matas, Jiri ^a  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

mean shift; object tracking; scale estimation

Indexed keywords

BACKGROUND CLUTTER; MEAN SHIFT; MEAN-SHIFT TRACKING; OBJECT TRACKING; REGULARIZATION TERMS; SCALE ESTIMATION; STATE-OF-THE-ART ALGORITHMS; THEORETICAL DERIVATIONS;

IMAGE ANALYSIS; TRACKING (POSITION);

ALGORITHMS;

EID: 84884487630     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38886-6_61     Document Type: Conference Paper

References (16)

1. Bradski, G.R.: Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal Q2 (1998)
2. Collins, R.T.:Mean-shift blob tracking through scale space. In: Computer Vision and Pattern Recognition, pp. 234-240. IEEE Computer Society (2003)
3. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift, pp. 142-149 (2000)
4. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory 21(1), 32-40 (1975)
5. Hare, S., Saffari, A., Torr, P.: Struck: Structured output tracking with kernels. In: International Conference Computer Vision, pp. 263-270 (November 2011)
6. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints. In: Conference on Computer Vision and Pattern Recognition (2010)
7. Klein, D.A., Schulz, D., Frintrop, S., Cremers, A.B.: Adaptive real-time video-tracking for arbitrary objects. In: Intelligent Robots and Systems, pp. 772-777 (October 2010)
8. Liang, D., Huang, Q., Jiang, S., Yao, H., Gao, W.: Mean-shift blob tracking with adaptive feature selection and scale adaptation. In: International Conference Image Processing (2007)
9. Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. Computer Vision, IET 6(1), 62-69 (2012)
10. Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. Computer Vision, IET 6(1), 52-61 (2012)
11. Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. Computer Vision, IET 6(1), 52-61 (2012)
12. Pu, J.-X., Peng, N.-S.: Adaptive kernel based tracking using mean-shift. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 394-403. Springer, Heidelberg (2006) (Pubitemid 44553508)
13. Čehovin, L., Kristan, M., Leonardis, A.: An adaptive coupled-layer visual model for robust visual tracking. In: 13th International Conference on Computer Vision (November 2011)
14. Yang, C., Duraiswami, R., Davis, L.: Efficient mean-shift tracking via a new similarity measure. In: Computer Vision and Pattern Recognition, vol. 1, pp. 176-183 (June 2005)
15. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864-877. Springer, Heidelberg (2012)
16. Zhao, C., Knight, A., Reid, I.: Target tracking using mean-shift and affine structure. In: ICPR, pp. 1-5 (2008)