From Collective Attribute Association of Groups to Precise Attribute Association of Individuals

Abstract

Obscured person re-identification (Re-ID) aims to match an obscured image with a complete image of the same person captured by other cameras. As a major challenge in person identification, occlusion severely affects the effectiveness of most traditional person Re-ID methods. To solve this problem, this study proposes a trajectory association method, which, as a pre-processing technique for person Re-ID, can narrow the search range and reduce the problem of degradation caused by mixing. We investigate the method of converting the fuzzy association between sets into the precise association between elements for M video objects and N phone objects (trajectory information) with fuzzy group association relationships at the crime scene. First, we decompose the M-N precise association problem and analyze the similarity of the video objects in the source point and on the trajectories. Then, we define high-similarity points, study their distribution characteristics in different trajectories, and find that there is a significant difference between the distribution of high-similarity points in correct and incorrect matching trajectories. We simplify the full-path association problem into a partial-path high-similarity point distribution difference problem, which effectively reduces the difficulty in accurate association relationship construction. The association experiments in simple and mixed scenarios as well as Re-ID experiments on the PRPW and Market1501 demonstrate the effectiveness of our method.