Multi-patch matching for Person Re-identification

摘要

Recognizing a target object across non-overlapping distributed cameras is known in the computer vision community as the problem of person re-identification. In this paper, a multi-patch matching method for person re-identification is presented. Starting from the assumption that: the appearance (clothes) of a person does not change during the time of passing in different cameras field of view , which means the regions with the same color in target image will be identical while crossing cameras First, we extract distinctive features in the training procedure, where each image target is devised into small patches, the SIFT features and LAB color histograms are computed for each patch. Then we use the KNN approach to detect group of patches with high similarity in the target image and then we use a bi-directional weighted group matching mechanism for the re-identification. Experiments on a challenging VIPeR dataset show that the performances of the proposed method outperform several baselines and state of the art approaches.