Re-ranking pedestrian re-identification with multiple Metrics

Abstract

Pedestrian re-identification (re-ID) is a video surveillance technology for specific pedestrians in non-overlapping multi-camera scenes. However, due to the influence of dramatic changes in perspectives and pedestrian occasions, it is still a huge challenge to find a stable, reliable algorithm in high accuracy rate. In this paper, to increase the robustness and performance of re-ID, we proposed a re-ID method by re-ranking the refined re-ID results (i.e. initial lists) gotten from the kernel-Local Fisher Discriminant Analysis (kLFDA) and Marginal Fisher Analysis (MFA) metrics, which can improve the probability of the correct target on the initial result lists and also enhance the robustness. During the process of re-ranking, in order to distinguish pedestrians in high similarity, a rigorous distance constraint model named Perspective Distance Model (PDM) is designed to further reduce the intra-class variations and increase the distance of inter-class variations. By using the PDM, the concise results gotten from the kLFDA and MFA metrics are re-ranked in order to further recognize different individuals in high similarity and improve the re-ID rate. Experimental results on seven challenging re-ID datasets (VIPeR, CUHK01, Prid2011, iLIDS, CUHK03, Market-1501and DukeReID) show that the performance of proposed method is high and effective.