Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decentralized probabilistic approach to articulated body tracking
Computer Vision and Image Understanding
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean field approach for tracking similar objects
Computer Vision and Image Understanding
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
A Multiple-Hypothesis Approach for Multiobject Visual Tracking
IEEE Transactions on Image Processing
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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.