Adaptive Metric Learning in Local Distance Comparison for People Re-identification

Abstract

A local distance comparison for multiple-shot people re-identification based on a new adaptive metric learning method is introduced in this paper. There exist two intrinsic issues in multiple-shot person re-identification: Large variances in view point, illumination, and non-rigid deformation are included in the image set of the same person, only a few training data for learning tasks are available in a realistic re-identification scenario. We deal with the multimodal property of people's appearance distribution caused by the first issue by using a local distance comparison approach. Since the capability of the local distance comparison highly depends on the choice of distance metric, we also introduce an adaptive learning method to learn an appropriate distance metric and use it to find and compute local neighbors effectively. This adaptive learning method is able to solve the over fitting problem caused by the second issue, through leveraging the generic knowledge of re-identification together with the specific information of the target task. We evaluated our approach on public benchmark datasets, and confirmed its superiority as compared to conventional approaches.