Accurate appearance-based Bayesian tracking for maneuvering targets

Computer Vision and Image Understanding

Volumn 113, Issue 4, 2009, Pages 544-555

  Maggio, Emilio ^a   Cavallaro, Andrea ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Color histogram; Mean Shift; Object representation; Particle Filter; Tracking

Indexed keywords

AIR FILTERS; COLOR; FREQUENCY MODULATION; IMAGE RETRIEVAL; NONLINEAR FILTERING; TARGETS; VECTOR QUANTIZATION;

COLOR HISTOGRAM; MEAN SHIFT; OBJECT REPRESENTATION; PARTICLE FILTER; TRACKING;

TARGET TRACKING;

EID: 61349143045     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2008.12.003     Document Type: Article

References (43)

1. B. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in: Proceedings of International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
2. Zhou S.K., Chellappa R., and Moghaddam B. Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Processing 13 11 (2004) 1491-1506
3. Matthews I., Ishikawa T., and Baker S. The template update problem. IEEE Trans. Pattern Analysis Machine Intell. 26 6 (2004) 810-815
4. Nguyen H.T., and Smeulders A.W.M. Fast occluded object tracking by a robust appearance filter. IEEE Trans. Pattern Analysis Machine Intell. 26 8 (2004) 1099-1104
5. A. Jepson, D. Fleet, T. El-Maraghi, Robust online appearance models for visual tracking, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, Kauai, USA, 2001, pp. 415-422.
6. Black M., and Jepson A. Eigen-tracking: robust matching and tracking of articulated objects using a view-based representation. International Journal on Computer Vision 36 2 (1998) 63-84
7. Comaniciu D., Ramesh V., and Meer P. Kernel-based object tracking. IEEE Trans. Pattern Analysis Machine Intell. 25 5 (2003) 564-577
8. P. Perez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: Proceedings of the European Conference on Computer Vision, Copenhagen, DK, vol. 1, 2002, pp. 661-675.
9. S. Birchfield, Elliptical head tracking using intensity gradients and color histograms, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, USA, 1998, pp. 232-237.
10. M. Isard, J. MacCormick, Bramble: a bayesian multiple-blob tracker, in: Proceedings of International Conference on Computer Vision, Vancouver, CAN, 2001, pp. 34-41.
11. S. Birchfield, S. Rangarajan, Spatiograms versus histograms for region-based tracking, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005, pp. 1158-1163.
12. K. Okuma, A. Taleghani, N.D. Freitas, J. Little, D.G. Lowe, A boosted particle filter: multitarget detection and tracking, in: Proceedings of the European Conference on Computer Vision, Prague, CZ, 2004, pp. 28-39.
13. E. Maggio, A. Cavallaro, Multi-part target representation for colour tracking, in: Proceedings of IEEE International Conference on Image Processing, Genoa, IT, 2005, pp. 729-732.
14. Liu T., and Chen H. Real-time tracking using trust-region methods. IEEE Trans. Pattern Analysis Machine Intell. 26 3 (2004) 397-402
15. E. Maggio, F. Smeraldi, A. Cavallaro, Combining colour and orientation for adaptive particle filter-based tracking, in: Proceedings of British Machine Vision Conference, Oxford, UK, 2005, pp. 659-668.
16. 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 Processing 50 2 (2002) 174-188
17. Nummiaro K., Koller-Meier E., and Gool L.V. An adaptive color-based particle filter. Image and Vision Computing 21 1 (2002) 99-110
18. Isard M., and Blake A. CONDENSATION-conditional density propagation for visual tracking. International Journal on Computer Vision 29 1 (1998) 5-28
19. R. van der Merwe, A. Doucet, N. de Freitas, E. Wan, The unscented particle filter, Tech. Rep. CUED/F-INFENG/TR380, Cambridge University, Engineering Department, August 2000. Available from: .
20. K. Choo, D. Fleet, People tracking using hybrid Monte Carlo filtering, in: Proceedings of International Conference on Computer Vision, Vancouver, CAN, vol. 2, 2001, pp. 321-328.
21. J. Sullivan, J. Rittscher, Guiding random particles by deterministic search, in: Proceedings of International Conference on Computer Vision, Vancouver, CAN, vol. 1, 2001, pp. 323-330.
22. C. Chang, R. Ansari, Kernel particle filter: iterative sampling for efficient visual tracking, in: Proceedings of IEEE International Conference on Image Processing, Barcelona, SP, vol. 3, 2003, pp. III-977-980.
23. B. Han, D. Comaniciu, Y. Zhu, L. Davis, Incremental density approximation and kernel-based bayesian filtering for object tracking, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, USA, vol. 1, 2004, pp. 638-644.
24. C. Sminchisescu, B. Triggs, Covariance scaled sampling for monocular 3d body tracking, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, USA, vol. 1, 2001, pp. 447-454.
25. C. Sminchisescu, B. Triggs, Hyperdynamics importance sampling, in: Proceedings of the European Conference on Computer Vision, Copenhagen, DK, vol. 1, 2002, pp. 769-783.
26. J. Deutscher, A. Blake, I. Reid, Articulated body motion capture by annealed particle filtering, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Head Island, USA, vol. 2, 2000, pp. 126-133.
27. Li P., Zhang T., and Pece A.E.C. Visual contour tracking based on particle filters. Image and Vision Computing 21 1 (2003) 111-123
28. C. Shan, Y. Wei, T. Tan, F. Ojardias, Real time hand tracking by combining particle filtering and mean shift, in: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, Southampton, UK, 2004, pp. 669-674.
29. E. Maggio, A. Cavallaro, Hybrid particle filter and mean shift tracker with adaptive transition model, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, USA, vol. 2, 2005, pp. 221-224.
30. Kailath T. The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15 (1967) 52-60
31. Comaniciu D., and Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Analysis Machine Intell. 24 5 (2002) 603-619
32. Silverman B. Density Estimation for Statistics and Data Analysis (1986), Chapman and Hall, London
33. G. Pingali, J. Segen, Performance evaluation of people tracking systems, in: Proceedings of the IEEE Workshop on Applications of Computer Vision, Sarasota, USA, 1996, pp. 33-38.
34. V. Mariano, M. Junghye, P. Jin-Hyeong, R. Kasturi, D. Mihalcik, L. Huiping, D. Doermann, T. Drayer, Performance evaluation of object detection algorithms, in: Proceedings of IEEE Conference on Pattern Recognition, Quebec, CAN, vol. 3, 2002, pp. 965-969.
35. C. Needham, R. Boyle, Performance evaluation metrics and statistics for positional tracker evaluation, in: Proceedings of International Conference on Computer Vision Systems, New York, USA, 2003, pp. 278-289.
36. J. Black, T. Ellis, P. Rosin, A novel method for video tracking performance evaluation, in: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), Nice, FR, 2003, pp. 125-132.
37. T. Schlogl, C. Beleznai, M. Winter, H. Bischof, Performance evaluation metrics for motion detection and tracking, in: Proceedings of IEEE Conference on Pattern Recognition, Surrey, UK, vol. 4, 2004, pp. 519-522.
38. D. Doermann, D. Mihalcik, Tools and techniques for video performance evaluation, in: Proceedings of IEEE Conference on Pattern Recognition, Barcelona, SP, vol. 4, 2000, pp. 167-170.
39. C. Jaynes, S. Webb, R. Steele, Q. Xiong, An open development environment for evaluation of video surveillance systems, in: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), Copenhagen, DK, 2002, pp. 32-39.
40. R. Collins, X. Zhou, S. Teh, An open source tracking testbed and evaluation web site, in: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), Breckenridge, USA, 2005.
41. Y. Wu, G. Hua, T. Yu, Switching observation models for contour tracking in clutter, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Madison, USA, 2003, pp. 295-304.
42. Fukunaga K., and Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inform. Theory 21 1 (1975) 32-40
43. D. Comaniciu, V. Ramesh, P. Meer, Real-time tracking of non-rigid objects using mean shift, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Head Island, USA, vol. 2, 2000, pp. 142-149.