Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs
Abstract—Addresses a new person reidentification problem without label information of persons under nonoverlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched < Final Year Projects 2016 > and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidentification under target domain cameras without person labels. 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 reidentification. Inspired by adaptive learning methods, a new discriminative model with high confidence in target positive mean and low confidence in target negative image pairs is developed by refining the distance model learnt from the source domain.
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