A Sampling Algorithm for Tracking Multiple Objects
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.
Unable to display preview. Download preview PDF.
- 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.Google Scholar
- Z. Ghahramani and G. E. Hinton, “Parameter estimation for linear dynamical systems,” Technical Report CRG-TR-96-2, Univ. of Toronto, 1996.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- J. Sullivan, A. Blake, M. Isard, and J. MacCormick, “Object localization by Bayesian correlation,” Proc. Int. Conf. Computer Vision, 1999.Google Scholar
- J. MacCormick and A. Blake, “A probabilistic exclusion principle for tracking multiple objects,” Proc. Int. Conf. Computer Vision, 1999.Google Scholar
- N. A. C. Cressie, Statistics for Spatial Data, John Wiley & Sons Inc., 1991.Google Scholar
- 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.Google Scholar