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We present a robust probabilistic method to classify targets based on their tracks. As is customary in supervised learning problems, it is assumed that example tracks from various classes are available to train a classifier. We present an optimal but computationally intensive sequential solution, and show that a computationally feasible naive Bayes approximation works better than ignoring sequential...
A new multi-target tracking algorithm, termed as the Gaussian sum convolution probability hypothesis density (GSCPHD) filter, is proposed. The filter is calculated by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. It is shown that the ability to deal with complex observation model, non or small observation noise of the GSCPHD over the Gaussian...
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