Abstract
Due to its great ability of conquering clutters, which is especially useful for high-dimensional tracking problems, particle filter becomes popular in the visual tracking community. One remained difficulty of applying the particle filter to high-dimensional tracking problems is how to propagate particles efficiently considering complex motions of the target. In this paper, we propose the idea of approximating the complex motion model using a set of simple motion models to deal with the tracking problems cumbered by complex motions. Then, we provide a practical way to do inference on the set of simple motion models instead of original complex motion model in the particle filter. This new variation of particle filter is termed as Multi-Model Particle Filter (MMPF). We apply our proposed MMPF to the problem of head motion tracking. Note that the defined head motions include both rigid motions and non-rigid motions. Experiments show that, when compared with the standard particle filter, the MMPF works well for this high-dimensional tracking problem with reasonable computational cost. In addition, the MMPF may provide a possible solution to other high-dimensional sequential state estimation problems such as human body pose estimation and sign language estimation and recognition from video.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Isard, M., Blake, A.: CONDENSATION – conditional density propagation for visual tracking. Internal Journal of Computer Vision 29(1), 5–28 (1998)
Forsyth, D.A., Ponce, J.: Computer Vision: A modern approach. Prentice Hall, Englewood Cliffs (2002)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using Mean Shift. In: IEEE Proceedings of CVPR, Hilton Head Island, South Carolina, vol. 2, pp. 142–149 (2000)
MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface- quality hand tracker. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Rasmussen, C., Hager, G.: Probabilistic Data Association Methods for Tracking Complex Visual Objects. IEEE Transactions on PAMI 23(6) (June 2001)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealled particle filtering. In: IEEE Proceedings of CVPR, Hilton Head, V II, pp. 126–133 (2000)
Choo, K., Fleet, D.J.: People tracking using hybrid monte carlo filtering. In: IJCV (2001)
Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic tracking of 3d human figures using 2d image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)
Pavlovic, V., Rehg, J.M., Cham, T.-J., Murphy, K.P.: A dynamic bayesian network approach to figure tracking using learned dynamic models. In: ICCV (1999)
Rohr, K.: Human movement analysis based on explicit motion models. In: Motion- Based Recognition, ch. 8, pp. 171–198. Kluwer Aademic Publishers, Dordrecht (1997)
Ostermann, J.: Animation of Synthetic Faces in MPEG-4, Computer Animation, pp. 49–51, June 8-10 (1998)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proc. of SIGGRAPH 1999 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J., Zhao, D., Shan, S., Gao, W. (2004). Approximating Inference on Complex Motion Models Using Multi-model Particle Filter. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30542-2_124
Download citation
DOI: https://doi.org/10.1007/978-3-540-30542-2_124
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23977-2
Online ISBN: 978-3-540-30542-2
eBook Packages: Computer ScienceComputer Science (R0)