Abstract
Particle Filter methods are one of the dominant tracking paradigms due to its ability to handle non-gaussian processes, multimodality and temporal consistency. Traditionally, the exponential growth on the number of particles required (and therefore in the computational cost) with respect to the increase of the state space dimensionality means one of the major drawbacks for these methods. The problem of part based tracking, central nowadays, is hardly tractable within this framework. Several efforts have been made in order to solve this problem, as the appearance of hierarchical models or the extension of graph theory by means of the Nonparametric Belief Propagation. Our approach relies instead on the use of Auxiliary Particle Filters, models the relations between parts dynamically (without training) and introduces a compatibility factor to efficiently reduce the growth of the computational cost. We did run the experiments presented without using a priori information.
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
Deutscher, J., Davison, A., Reid, I.: Automatic partitioning of high dimensional search spaces associated with articulated body motion capture. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 669–676 (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)
Gu, L., Xing, E.P., Kanade, T.: Learning gmrf structures for spatial priors. In: Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007)
McCall, J.C., Trivedi, M.M.: Facial action coding using multiple visual cues and a hierarchy of particle filters. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p. 150 (2006)
Park, M., Liu, Y., Collins, R.: Efficient Mean Shift Belief Propagation for Vision Tracking. In: Conference on Computer Vision and Pattern Recognition (2008)
Patras, I., Pantic, M.: Particle filtering with factorized likelihoods for tracking facial features. In: International Conference on Automatic Face and Gesture Recognition, pp. 97–102 (2004)
Pitt, M.K., Shephard, N.: Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association 94(446), 590–599 (1999)
Su, C., Zhuang, Y., Huang, L., Wu, F.: A two-step approach to multiple facial feature tracking: temporal particle filter and spatial belief propagation. In: International Conference on Automatic Face and Gesture Recognition, pp. 433–438 (2004)
Sudderth, E.B., Ihler, A.T., Freeman, W.T., Willsky, A.S.: Nonparametric Belief Propagation. In: Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-605–I-612 (2003)
Vlassis, N., Terwijn, B., Krse, B.: Auxiliary particle filter robot localization from high-dimensional sensor observations. In: International Conference on Robotics and Automation, pp. 7–12 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martinez, B., Vivet, M., Binefa, X. (2010). Compatible Particles for Part-Based Tracking. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2010. Lecture Notes in Computer Science, vol 6169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14061-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-14061-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14060-0
Online ISBN: 978-3-642-14061-7
eBook Packages: Computer ScienceComputer Science (R0)