Adaptive weighting of local classifiers by particle filters for robust tracking

Pattern Recognition

Volumn 42, Issue 5, 2009, Pages 619-628

  Hotta, Kazuhiro ^a  

^a UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

Author keywords

Adaptive; Combination of local classifiers; Partial occlusion; Particle filter; Robust; Tracking; Weighting

Indexed keywords

AIR FILTERS; DISTRIBUTED COMPUTER SYSTEMS; LEARNING SYSTEMS; NONLINEAR FILTERING; SIGNAL FILTERING AND PREDICTION; TARGET TRACKING; WEIGHING;

ADAPTIVE; COMBINATION OF LOCAL CLASSIFIERS; PARTIAL OCCLUSION; PARTICLE FILTER; ROBUST; TRACKING; WEIGHTING;

CLASSIFIERS;

EID: 58249084583     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.09.026     Document Type: Article

References (28)

1. Schneiderman H., and Kanade T. A statistical method for 3d object detection applied to faces and cars. Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2000) 746-751
2. Mohan A., Papageorgiou C., and Poggio T. Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23 4 (2001) 349-361
3. Heisele B., Serre T., Pontil M., and Poggio T. Component-based face detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2001) 657-662
4. Viola P., and Jones M. Robust real-time face detection. Int. J. Comput. Vision 57 2 (2004) 137-154
5. Hotta K. A robust face detector under partial occlusion. Proceedings of the IEEE International Conference on Image Processing (2004) 597-600
6. Kurita T., Hotta K., and Mishima T. Feature ordering by cross validation for face detection. Proceedings of the IAPR Workshop on Machine Vision Applications (2000) 211-214
7. Collins R., Liu Y., and Leordeanu M. On-line selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27 10 (2005) 1631-1643
8. Hand B., and Davis L. Object tracking by adaptive feature extraction. Proceedings of the International Conference on Image Processing (2004) 1501-1504
9. Chen H.-T., Liu T.-L., and Fuh C.-S. Probabilistic tracking with adaptive feature selection. Proceedings of the International Conference on Pattern Recognition (2004) 736-739
10. Isard M., and Blake A. Condensation-conditional density propagation for visual tracking. Int. J. Comput. Vision 29 1 (1998) 5-28
11. Doucet A., de Freitas N., and Gordon N. Sequential Monte Carlo Methods in Practice (2001), Springer, Berlin
12. Ristic B., and Arulampalam S. Beyond the Kalman Filter (2004), Artech House
13. Hotta K. Adaptive weighting of local classifiers. Proceedings of the 18th International Conference on Pattern Recognition (2006) 610-613
14. Cristinacce D., and Cootes T. Feature detection and tracking with constrained local models. Proceedings of the British Machine Vision Conference (2006) 929-938
15. Gross R., Matthews I., and Baker S. Active appearance models with occlusion. Image Vision Comput. 24 (2006) 593-604
16. Loutas E., Pitas I., and Nikou C. Probabilistic multiple face detection and tracking using entropy measures. IEEE Trans. Circuits Syst. Video Technol. 14 1 (2004) 128-135
17. C. Tomasi, T. Kanade, Shape and motion from image streams: a factorization method-part 3: detection and tracking of point features, Technical Report, CMU-CS-91-132, 1991.
18. Özuysal M., Lepetit V., Fleuret F., and Fua P. Feature harvesting for tracking-by-detection. Proceedings of the European Conference on Computer Vision (2006) 592-605
19. Lades M., Vorbrüggen J.C., Buhmann J., Lange J., Malsburg C.v.d., Würtz R.P., and Konen W. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42 3 (1993) 300-311
20. Jones J., and Palmer L. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in the cat striate cortex. J. Neurophysiol. 58 (1987) 1233-1258
21. Olshausen B.A., and Field D.J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381 13 (1996) 607-609
22. Bell A., and Sejnowski T. The 'independent components' of natural scenes are edge filters. Vision Res. 37 23 (1997) 3327-3338
23. Hotta K. View independent face detection based on combination of local and global kernels. Proceedings of the International Conference on Computer Vision Systems (2007) 1-10
24. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
25. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge University Press, Cambridge
26. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press, Cambridge
27. Hotta K. Fast, accurate and robust recognition based on local normalized linear summation kernel. Proceedings of the Digital Image Computing: Techniques and Applications (DICTA2007) (2007) 470-477
28. Comaniciu D., Ramesh V., and Meer P. Real-time tracking of non-rigid objects using mean shift. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2000) 142-149