Online multiple instance gradient feature selection for robust visual tracking

Pattern Recognition Letters

Volumn 33, Issue 9, 2012, Pages 1075-1082

  Xie, Yuan ^a   Qu, Yanyun ^b   Li, Cuihua ^b   Zhang, Wensheng ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b XIAMEN UNIVERSITY   (China)

Author keywords

Gradient based feature selection; HOG; Multiple Instance Learning; Online object tracking

Indexed keywords

ADAPTIVE APPEARANCE MODELS; APPEARANCE MODELS; BINARY CLASSIFICATION PROBLEMS; DISCRIMINATIVE FEATURES; GRADIENT BASED; HOG; MULTIPLE INSTANCE LEARNING; MULTIPLE INSTANCES; OBJECT TRACKING; OPTIMIZATION SCHEME; ROBUST TRACKING; SELECTION MECHANISM; TRACKING TECHNIQUES; UNIFIED FRAMEWORK; VIDEO SEQUENCES; VISUAL TRACKING;

LEARNING SYSTEMS;

TRACKING (POSITION);

EID: 84859309044     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2012.01.020     Document Type: Article

References (30)

1. Tieu, K., Viola, P., 2000. Boosting image retrieval. In: Proc. Computer Vision and Pattern Recognition, pp. 228-235.
2. Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: Proc. Computer Vision and Pattern Recognition, pp. 511-518.
3. Opelt, A., Fussenegger, M., Pinz, A., Auer, P., 2004. Weak hypotheses and boosting for generic object detection and recognition. In: Proc. European Conf. on Computer Vision, pp. 71-84.
4. Torralba, A., Murphy, K., Freeman, W., 2005. Sharing features: Efficient boosting procedures for multi-class object detection. In: Proc. Computer Vision and Pattern Recognition, pp. 762-769.
5. Yang, P., Shan, S., Gao, W., Li, S., Zhang, D., 2004. Face recognition using Adaboosted Gabor features. In: Proc. Automatic Face and Gesture Recognition, pp. 356-361.
6. S. Avidan Ensemble tracking IEEE Trans. Pattern Anal. Machine Intell. 2007 261 271 (Pubitemid 46420886)
7. Grabner, H., Grabner, M., Bischof, H., 2006. Real-time tracking via online boosting. In: Proc. British Machine Vision Conference, pp. 47-56.
8. Grabner, H., Bischof, H., 2006. Online boosting and vision. In: Proc. Computer Vision and Pattern Recognition, pp. 260-267.
9. Liu, X., Yu, T., 2007. Gradient feature selection for online boosting. In: Proc. Internat. Conf. on Computer Vision, 2007.
10. Dalal, N., Triggs, B., 2005. Histograms of oriented gradient for human detection. In: Proc. Computer Vision and Pattern Recognition, pp. 886-893. (Pubitemid 43897286)
11. Viola, P., Platt, J.C., Zhang, C., 2005. Multiple instance boosting for object detection. In: Proc. Neural Information Processing Systems, pp. 1417-1426.
12. Babenko, B., Yang, M.H., Belongie, S., 2009. Visual tracking with online multiple instance learning. In: Proc. Computer Vision and Pattern Recognition.
13. R.T. Collins, Y. Liu, and M. Leordeanu Online selection of discriminative tracking features IEEE Trans. Pattern Anal. Machine Intell. 2005 1631 1643 (Pubitemid 41555407)
14. T.G. Dietterich, R.H. Lathrop, and L.T. Perez Solving the multiple instance problem with axis parallel rectangle Artif. Intell. 1997 31 71 (Pubitemid 127412230)
15. Andrews, S., Tsochantaridis, L., Hofmann, T., 2003. Support vector machines for multiple instance learning. In: Proc. Neural Information Processing Systems, pp. 577-584.
16. Adam, A., Rivlin, E., Shimshoni, I., 2006. Robust fragments-based tracking using the integral histogram. In: Proc. Computer Vision and Pattern Recognition, pp. 798-805. (Pubitemid 44931406)
17. Lin, R., Ross, D., Lim, J., Yang, M.H., 2004. Adaptive discriminative generative model and its applications. In: Proc. Neural Information Processing Systems, pp. 801-808.
18. D. Ross, J. Lim, R.S. Lin, and M.H. Yang Incremental learning for robust visual tracking Int. J. Comput. Vis. 2008 125 141 (Pubitemid 351294740)
19. Laptev, I., 2004. Improvements of object detection using boosted histograms. In: Proc. British Machine Vision Conference, pp. 53-60.
20. Comaniciu, D., Ramesh, V., Meer, P., 2000. Real-time tracking of non-grid objects using mean shift. In: Proc. Computer Vision and Pattern Recognition, pp. 142-149.
21. Isard, M., Maccormick, J., 2001. Bramble: A Bayesian multiple-blob tracker. In: Proc. International Conference on Computer Vision, pp. 34-41. (Pubitemid 32795038)
22. Fatih, P., 2005. Integral histogram: A fast way to extract histograms in Cartesian spaces. In: Proc. Computer Vision and Pattern Recognition.
23. Fergus, R., Perona, P., Zisserman, A., 2005. A sparse object category model for efficient learning and exhaustive recognition. In: Proc. Computer Vision and Pattern Recognition.
24. B. Leibe, K. Schindler, N. Cornelis, and L. Van Gool Coupled object detection and tracking from static cameras and moving vehicles IEEE Trans. Pattern Anal. Machine Intell. 2008 1683 1698
25. Kwon, J., Mu Lee, K., 2009. Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping Monte Carlo sampling. In: Proc. Computer Vision and Pattern Recognition.
26. A.D. Jepson, D.J. Fleet, and T.F. El-Maraghi Robust online appearance models for visual tracking IEEE Trans. Pattern Anal. Machine Intell. 2003 1296 1311
27. Wang, J., Chen, X., Gao, W., 2005. Online selecting discriminative tracking features using particle filter. In: Proc. Computer Vision and Pattern Recognition, pp. 1037-1042.
28. S. Avidan Support vector tracking IEEE Trans. Pattern Anal. Machine Intell. 2001 73 77
29. Kalal, Z., Matas, J., Mikolajczyk, K., 2009. Online learning of robust object detectors during unstable tracking. In: Proc. International Conference on Computer Vision Workshop.
30. Kalal, Z., Matas, J., Mikolajczyk, K., 2010. P-N learning: Bootstrapping binary classifiers by structural constraints. In: Proc. Computer Vision and Pattern Recognition.