An Unsupervised Multi-Shot Person Re-Identification Method via Mutual Normalized Sparse Representation and Stepwise Learning

Due to abundant prior information and widespread applications, multi-shot based person re-identification has drawn increasing attention in recent years. In this paper, the high labeling cost and huge unlabeled data motivate us to focus on the unsupervised scenario and a unified coarse-to-fine framework is proposed, named by Mutual Normalized Sparse Representation (MNSR). The authors' method is an iteration procedure and each iteration involves two key steps: label estimation and metric model learning. In the former, they present a MNSR model to infer the pairwise labels of cross-camera by endowing sparse representation coefficient with the probability property. MNSR explicitly takes the mutually correlation between cameras into consideration and thus produces more accurate results. Meanwhile, they propose a probability-guided positive pairwise label prediction method to mine hard positive samples. For the latter, they learn a metric model with the estimated pairwise labels as supervision. In this procedure, they select some reliable labels for training by configuring with a stepwise learning method, rather than use all the estimated pair samples. This procedure helps to prevent the noise samples damaging the learning of discriminative metric model, especially for the initial iterations. Extensive experiments are conducted on four publicly available datasets, including PRID 2011, iLIDS-VID, SAIVT-SoftBio and MARS, and the results demonstrate the superior performance of the MNSR method in comparison with state-of-the-art unsupervised multi-shot person re-identification methods.


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  • Accession Number: 01860160
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 30 2022 2:27PM