摘要： This paper addresses a new person reidenti?cation 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 (negative) and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidenti?cation 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 reidenti?cation. Inspired by adaptive learning methods, a new discriminative model with high con?dence in target positive mean and low con?dence in target negative image pairs is developed by re?ning the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidenti?cation methods without using label information in target cameras. Moreover, our method achieves better reidenti?cation performance than existing domain adaptation methods derived under equal conditional probability assumption.