Occlusion Management in Sequential Mean Field Monte Carlo Methods
In this paper we analyse the problem of occlusions under a Mean Field Monte Carlo approach. This kind of approach is suitable to approximate inference in problems such as multitarget tracking, in which this paper is focused. It leads to a set of fixed point equations, one for each target, that can be solved iteratively. While previous works considered independent likelihoods and pairwise interactions between objects, in this work we assume a more realistic joint likelihood that helps to cope with occlusions. Since the joint likelihood can truly depend on several objects, a high dimensional integral appears in the raw approach. We consider an approximation to make it computationally feasible. We have tested the proposed approach on football and indoor surveillance sequences, showing that a low number of failures can be achieved.
KeywordsMultitarget tracking occlusions mean field
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- 1.Isard, M., MacCormick, J.: BraMBLe: A Bayesian Multiple-Blob Tracker. In: Proceedings of the IEEE ICCV, vol. 2, pp. 34–41 (2001)Google Scholar
- 3.Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Transactions on PAMI 30(7) (2008)Google Scholar
- 7.Yao, J., Odobez, J.M.: Multi-Camera Multi-Person 3D Space Tracking with MCMC in Surveillance Scenarios. In: ECCV Workshop on Multi Camera and Multi-modal Sensor Fusion Algorithms and Applications ECCV-M2SFA2 (2008)Google Scholar