Date of Publication :7th March 2016

Abstract: An adaptive ranking support vector machines (AdaRSVMs) method is used for re identification under target domain cameras without person labels. It addresses a new person re identification problem without label information of persons under non overlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) and unlabeled image pairs from target domain cameras, To overcome the problems introduced due to the absence of matched (positive) image pairs in the target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the available data from source and target domains as well as constraints in person re identification. Inspired by adaptive learning methods, a new discriminative model with high confidence in target positive mean and low confidence in v target negative image pairs is developed by refining the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning re identification methods without using label information in target cameras. Moreover, our method achieves better re identification performance than existing domain adaptation methods derived under equal conditional probability assumption.

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