An incremental Bhattacharyya dissimilarity measure for particle filtering

Pattern Recognition

Volumn 43, Issue 4, 2010, Pages 1244-1256

  Yao, Anbang ^a   Wang, Guijin ^a   Lin, Xinggang ^a   Chai, Xiujuan ^b  

^a TSINGHUA UNIVERSITY   (China)

^b NOKIA RESEARCH CENTER   (United States)

Author keywords

Bhattacharyya dissimilarity; Mixture of Gaussian; Particle filtering; Visual tracking

Indexed keywords

BACKGROUND CLUTTER; CRUCIAL PARAMETERS; DESCRIPTORS; DISSIMILARITY MEASURES; MIXTURE OF GAUSSIANS; PARTIAL OCCLUSIONS; PARTICLE FILTERING; ROBUST TRACKING; SIMILARITY MATRIX; STATE OF THE ART; TARGET MODEL; TEMPORAL ATTRIBUTES; TRACKING PERFORMANCE; VISUAL TRACKING; WEIGHT ESTIMATION;

BINS; TARGETS;

NONLINEAR FILTERING;

EID: 74449093898     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.09.024     Document Type: Article

References (43)

1. Zhao T., and Nevatia R. Tracking multiple humans in complex situations. IEEE Trans. Pattern Anal. Mach. Intell. 26 9 (2004) 1208-1221
2. Hu W., Xiao X., Xie D., Tan T., and Maybank S. Traffic accident prediction using 3-D model-based vehicle tracking. IEEE Trans. Veh. Tecnol. 53 3 (2004) 677-694
3. Stenger B., Thayananthan A., Torr P.H.S., and Cipolla R. Model-based hand tracking using a hierarchical Bayesian filter. IEEE Trans. Pattern Anal. Mach. Intell. 28 9 (2006) 1372-1384
4. Z. Yin, R. Collins, On-the-fly object modeling and tracking, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8.
5. Yilmaz A., and Shah M. Object tracking: a survey. ACM Comput. Surv. 38 4 (2006) 1-45
6. Isard M., and Blake A. Condensation-conditional density propagation for visual tracking. Int. J. Comput. Vision 29 14 (1998) 5-28
7. Kalman R.E. A new approach to linear filtering and prediction theory. Trans. ASME Ser. D J. Basic Eng. 82 (1960) 35-45
8. Arulampalam M.S., Maskell S., Gordon N., and Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50 2 (2002) 174-188
9. B. Han, D. Comaniciu, Y. Zhu, L.S. Davis, Incremental density approximation kernel-based Bayesian filtering for object tracking, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2004, pp. 638-644.
10. Francesc M.N., Sanfeliu A., and Samaras D. Dependent multiple cue integration for robust tracking. IEEE Trans. Pattern Anal. Mach. Intell. 30 4 (2008) 670-685
11. Li Y., Ai H., Yamashita T., Lao S., and Kawade M. Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans. Pattern Anal. Mach. Intell. 30 10 (2008) 1720-1740
12. K. Nummiaro, E.B. Koller-Meier, L.V. Gool, Object tracking with an adaptive color-based particle filter, in: Proceedings of Symposium for Pattern Recognition of the DAGM, 2002, pp. 355-360.
13. Nummiaroa K., Koller-Meierb E., and Gool L.V. An adaptive color-based particle filter. Image Vision Comput. 21 1 (2003) 99-110
14. P. Perez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: Proceedings of ECCV, 2002, pp. 661-675.
15. D. Comaniciu, V. Ramesh, P. Meer, Real-time tracking of non-rigid objects using mean shift, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2000, pp. 142-149.
16. Collins R.T., Liu Y., and Leordeanu M. Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27 10 (2008) 1631-1643
17. X. Liu, T. Yu, Gradient feature selection for online boosting, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2007, pp. 1-8.
18. C. Yang, R. Duraiswami, L.S. Davis, Fast multiple object tracking via a hierarchical particle filter, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2005, pp. 212-219.
19. C. Yang, R. Duraiswami, L.S. Davis, Efficient mean-shift tracking via a new similarity measure, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2005, pp. 176-183.
20. A. Elgammal, R. Duraiswami, L.S. Davis, Probabilistic tracking in joint feature-spatial spaces, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2003, pp. 781-788.
21. T.M. Cover, J.A. Thomas, Elements of information theory, in: Wiley Series in Telecommunications, Wiley, New York, USA, 1991.
22. Wang H., Suder D., Schindler K., and Shen C. Adaptive object tracking based on an effective appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 29 9 (2007) 1661-1667
23. Hafner J., Sawhney H.S., Equitz W., Flickner M., and Niblack W. Efficient color histogram indexing for quadratic form distance functions. IEEE Trans. Pattern Anal. Mach. Intell. 17 7 (1995) 729-736
24. Rubner Y., Tomasi C., and Guibas L.J. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision 40 2 (2000) 99-121
25. H. Ling, K. Okada, Diffusion distance for histogram comparison, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2006, pp. 246-253.
26. J. Puzicha, J.M. Buhmann, C. Tomasi, Y. Rubner, Empirical evaluation of dissimilarity measures for color and texture, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 1999, pp. 1165-1173.
27. J. Puzicha, T. Hofmann, J.M. Buhmann, Non-parametric similarity measures for unsupervised texture segmentation and image retrieval, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 1997, pp. 267-272.
28. Nicu S., Qi T., Michael S.L., and Thomas S.H. Similarity matching in computer vision and multimedia. Comput. Vision Image Understanding 100 3 (2008) 309-311
29. S. T. Birchfield, S. Rangarajan, Spatiograms versus histograms for region-based tracking, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2005, pp. 1158-1163.
30. J. Lichtenauer, M. Reinders, E. Hendriks, Influence of the observation likelihood function on particle filtering performance in tracking applications, in: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FGR), 2004, pp. 767-772.
31. Kailath T. The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15 1 (1967) 52-60
32. Geman D., German S., Graffigne C., and Dong P. Boundary detection by constrained optimization. IEEE Trans. Pattern Anal. Mach. Intell. 12 7 (1990) 609-628
33. Jain A.K. On an estimate of the Bhattacharyya distance. IEEE Trans. Syst. Man Cybern. 6 11 (1976) 763-766
34. Djouadi A., Snorrason O., and Garber F. The quality of training sample estimates of the Bhattacharyya coefficient. IEEE Trans. Pattern Anal. Mach. Intell. 12 1 (1990) 92-97
35. Lew M.S. Principles of Visual Information Retrieval (2001), Springer, New York
36. F. Porikli, Integral histogram: a fast way to extract histograms in Cartesian spaces, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2005, pp. 829-836.
37. F. Porikli, O. Tuzel, P. Meer, Covariance tracking using model update based on Lie algebra, in: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 2006, pp. 728-735.
38. 1: an efficient and robust algorithm for comparing histogram-based descriptors, in: Proceedings of ECCV, 2006, pp. 330-343.
39. 〈http://vision.ucla.edu/∼hbling/main.htm〉
40. E. Maggio, A. Cavallaro, Hybrid particle filter and mean shift tracker with adaptive transition model, in: Proceedings of the IEEE Acoustic, Speech and Signal Processing (ICASSP), 2005, pp. 221-224.
41. 〈http://groups.inf.ed.ac.uk/vision/caviar/caviardata1〉.
42. 〈http://vision.stanford.edu/∼birch/headtracker/seq〉.
43. 〈http://iris.usc.edu/∼icohen/projects/uav/index.xml〉.