Abstract
The recently proposed CONDENSATION algorithm and its variants enable the estimation of arbitrary multi-modal posterior distributions that potentially represent multiple tracked objects. However, the specific state representation adopted in the earlier work does not explicitly supports counting, addition, deletion and occlusion of objects. Furthermore, the representation may increasingly bias the posterior density estimates towards objects with dominant likelihood as the estimation progresses over many frames. In this paper, a novel formulation and an associated CONDENSATION-like sampling algorithm that explicitly support counting, addition and deletion of objects are proposed. We represent all objects in an image as an object configuration. The a posteriori distribution of all possible configurations are explored and maintained using sampling techniques. The dynamics of configurations allow addition and deletion of objects and handle occlusion. An efficient hierarchical algorithm is also proposed to approximate the sampling process in high dimensional space. Promising comparative results on both synthetic and real data are demonstrated.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, pp. 257–286, Feb. 1989.
Z. Ghahramani and G. E. Hinton, “Parameter estimation for linear dynamical systems,” Technical Report CRG-TR-96-2, Univ. of Toronto, 1996.
D. B. Reid, “An algorithm for tracking multiple targets,” IEEE Trans. Automatic Control, vol. 24, no. 6, pp. 843–854, Dec. 1979.
I. J. Cox, S. L.Hingorani, “An efficient implementation of Reid’s multiple hypothesis tracking algorithm and tts evaluation for the purpose of visual tracking,” IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 2, pp. 138–150, Feb 1996.
M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” in Proc. European Conf. on Computer Vision, pp. 343–356, Cambridge UK, 1996.
M. Isard and A. Blake, “ICONDENSATION: unified low-level and high-level tracking in a stochastic framework,” in Proc. European Conf. on Computer Vision, pp. 893–908, 1998.
J. Sullivan, A. Blake, M. Isard, and J. MacCormick, “Object localization by Bayesian correlation,” Proc. Int. Conf. Computer Vision, 1999.
J. MacCormick and A. Blake, “A probabilistic exclusion principle for tracking multiple objects,” Proc. Int. Conf. Computer Vision, 1999.
N. A. C. Cressie, Statistics for Spatial Data, John Wiley & Sons Inc., 1991.
A. Selinger and L. Wixson, “Classifying moving objects as rigid or non-rigid without correspondences,” Proc. DARPA Image Understanding Workshop, pp. 341–347, Monterey, CA, Nov. 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tao, H., Sawhney, H.S., Kumar, R. (2000). A Sampling Algorithm for Tracking Multiple Objects. In: Triggs, B., Zisserman, A., Szeliski, R. (eds) Vision Algorithms: Theory and Practice. IWVA 1999. Lecture Notes in Computer Science, vol 1883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44480-7_4
Download citation
DOI: https://doi.org/10.1007/3-540-44480-7_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67973-8
Online ISBN: 978-3-540-44480-0
eBook Packages: Springer Book Archive