A Hybrid Graphical Model for Online Multi-camera Tracking

We present a probabilistic method for online tracking of multiple objects with sparsely distributed cameras. The method explicitly identifies objects with latent labels and introduces latent states that represent intrinsic appearance properties. The dependency between observations, discrete labels and continuous states leads to a hybrid model, which induces appearance similarities and motion constraints between observations. Conditioned on the data we approximate the posterior probabilities of the labels with an efficient online assumed-density filtering (ADF) algorithm. Our model is demonstrated by tracking people with seven cameras. The experiments indicate that the ADF approximation is a competitive alternative for multiple hypothesis tracker (in terms of accuracy) and stochastic trackers (speed).

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