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Online-learned classifiers for robust multitarget tracking

In this paper, we propose online-learned classifiers for data association in multitarget tracking. The classifiers are dynamically constructed and incrementally online learned using image patches, which are associated based on location proimity. A biological inspired architecture is used to compute the classification label of image patch. The extracted image patches are coded and learned by a 3-layer neural network that implements in-place learning. We employ minimum-cost network flow optimization to associate tracks with the image patches based on their appearance and location proximities. The presented framework is applied to learn 11 targets encountered in a PETS2009 data set. Cross validation results show that the overall recognition accuracy is above 93%. The comparison with other learning algorithms is promising. The results of the implemented multitarget tracker demonstrate the effectiveness of the approach.