INTEGRATED METRIC LEARNING WITH ADAPTIVE CONSTRAINTS FOR PERSON RE-IDENTIFICATION

Person re-identification is an important technique to search a probe person against a set of gallery persons and metric learning methods have shown their effectiveness in matching person images. In this paper, an Integrated Metric Learning with Adaptive Constraints (IMLAC) method is proposed to promote the performance for person re-identification. In the method, the difference and commonness of an image pair are combined to define a novel integrated metric. Considering the complex variations of pedestrian images, a rule of adaptive pairwise constraints is extended for the integrated metric to further enhance separation and reunion between image pairs. Extensive experiments conducted on three person re-identification datasets including VIPeR, PRID450S and GRID indicate that the proposed method outperforms the state-of-the-art methods.