Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model

We propose a novel algorithm to solve the problem of person re-identification across multiple nonoverlapping cameras by grouping similarity comparison model. We use an image sequence instead of an image as a probe, and divide image sequence into groups by the method of systematic sampling. Then we design the rule which uses full-connection in a group and non-connection between groups to calculate similarities between images. We take the similarities as features, and train an AdaBoost classifier to match the persons across disjoint views. To enhance Euclidean distance discriminative ability, we propose a novel measure of similarity which is called Significant difference distance (SDD). Extensive experiments are conducted on two public datasets. Our proposed person re-identification method can achieve better performance compared with the state-of-the-art.