Dynamic partitioned sampling for tracking with discriminative features

British Machine Vision Conference, BMVC 2009 - Proceedings

Volumn , Issue , 2009, Pages

  Duffner, Stefan ^a   Odobez, Jean Marc ^a   Ricci, Elisa ^b  

^a IDIAP RESEARCH INSTITUTE   (Switzerland)

^b FONDAZIONE BRUNO KESSLER   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND PARTICLES; DISCRIMINATIVE FEATURES; HIERARCHICAL SAMPLING; HIGH-DIMENSIONAL; MULTI-CUE FUSIONS; PARTITIONED SAMPLING; SPECIFIC PROBLEMS; VIDEO SEQUENCES;

COMPUTER VISION;

EID: 84898877993     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.23.71     Document Type: Conference Paper

References (21)

1. S. O. Ba and J.-M. Odobez. A probabilistic framework for joint head tracking and pose estimation. In Proceedings of the International Conference on Pattern Recognition, Volume 4, pages 264-267, 2004.
2. V. Badrinarayanan, P. Perez, F. Le Clerc, and L. Oisel. Probabilistic color and adaptive multi-feature tracking with dynamically switched priority between cues. In Proceedings of the International Conference on Computer Vision, pages 1-8, October 2007.
3. S. Birchfield. Elliptical head tracking using intensity gradients and color histograms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 232-237, Santa Barbara, CA, USA, 1998.
4. P. Brasnett, L. Mihaylova, N. Canagarajah, and D. Bull. Particle filtering with multiple cues for object tracking in video sequences. In Proceedings of the SPIE, Volume 5685, pages 430-441, 2005.
5. R. T. Collins, Y. Liu, and M. Leordeanu. On-line selection of discriminative tracking features. IEEE Trans. on Pattern Analysis And Machine Intelligence, 27(10):1631-1643, 2005.
6. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Volume 1, pages 886-893, San Diego, CA, USA, 2005.
7. J. Deutscher, A. Blake, and I. Reid. Articulated body motion capture by annealed particle filtering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Volume 2, pages 126-133, 2000.
8. W. Du and J. H. Piater. A probabilistic approach to integrating multiple cues in visual tracking. In Proceedings of the European Conference on Computer Vision, pages 225-238, 2008.
9. Z. Khan, T. Balch, and F. Dellaert. Mcmc-based particle filter for tracking multiple interacting targets. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(11): 1805-1918, November 2005.
10. S. Z. Li. Markov Random Field Modeling in Computer Vision. Springer, London, UK, 1995.
11. Y. Li, H.Z. Ai, C. Huang, and S.H. Lao. Robust head tracking with particles based on multiple cues fusion. In Proceedings of the ECCV workshop on Human Computer Interaction, Volume 1, pages 29-39, 2006.
12. J. MacCormick and M. Isard. Partitioned sampling, articulated objects, and interface-quality hand tracker. In Proceedings of the European Conference on Computer Vision, Volume 2, pages 3-19, 2000.
13. E. Maggio, F. Smeraldi, and A. Cavallaro. Adaptive multi-feature tracking in a particle filtering framework. IEEE Trans. on Circuits and Systems for Video Technology, 10(7): 1348-1359, October 2007.
14. P. Perez, J. Vermaak, and A. Blake. Data fusion for visual tracking with particles. Proceedings of the IEEE, 92(3):495-513, 2004.
15. F. Porikli. Integral histogram: A fast way to extract higtograms in cartesian spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Volume 1, pages 829-836, 2005.
16. E. Ricci and J.M. Odobez. Learning large margin likelihoods for realtime head pose tracking. In Proceedings of the IEEE International Conference of Image Processing, 2009.
17. C. Shen, A. van den Hengel, and A. Dick. Probabilistic multiple cue integration for particle filter based tracking. In Proceedings of the 7th Digital Image Computing: Techniques and Applications, pages 10-12, 2003.
18. K. Smith and D. Gatica-Perez. Order matters: A distributed sampling method for multi-object tracking. In Proceedings of the British Maschine Vision Conference, London, September 2004.
19. Y. Wu and T. S. Huang. Robust visual tracking by integrating multiple cues based on co-inference learning. International Journal of Computer Vision, 58(1):55-71, 2004.
20. C. Yang, R. Duraiswami, and L. Davis. Fast multiple object tracking via a hierarchical particle filter. In Proceedings of the International Conference on Computer Vision, Volume 1, pages 212-219, 2005.
21. Z. Yin, F. Porikli, and R. Collins. Likelihood map fusion for visual object tracking. In Proceedings of the IEEE Workshop on Applications of Computer Vision, Volume 1, pages 1-7, 2008.