Discriminative tracking methods use binary classification to discriminate between the foreground and background and have\nachieved some useful results. However, the use of labeled training samples is insufficient for them to achieve accurate tracking.\nHence, discriminative classifiersmust use their own classification results to update themselves,whichmay lead to feedback-induced\ntracking drift. To overcome these problems, we propose a semisupervised tracking algorithm that uses deep representation and\ntransfer learning. Firstly, a 2D multilayer deep belief network is trained with a large amount of unlabeled samples. The nonlinear\nmapping point at the top of this network is subtracted as the feature dictionary. Then, this feature dictionary is utilized to transfer\ntrain and update a deep tracker. The positive samples for training are the tracked vehicles, and the negative samples are the\nbackground images. Finally, a particle filter is used to estimate vehicle position.We demonstrate experimentally that our proposed\nvehicle tracking algorithm can effectively restrain drift while also maintaining the adaption of vehicle appearance. Compared with\nsimilar algorithms, our method achieves a better tracking success rate and fewer average central-pixel errors.
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