Joint dictionary and metric learning for person re-identification

Abstract

Matching people across non-overlapping camera views, known as person re-identification, is of great importance for long-term pedestrian tracking in smart surveillance systems. Among various algorithms for person re-identification, dictionary learning is frequently utilized to build robust feature representation for images across different camera views. Metric learning, on the other hand, is usually exploited to find optimal feature subspace that maximizes the inter-person divergence while minimizes the intra-person divergence. Although both representative features and discriminative metrics have great impacts on the performance of person re-identification methods, most of the existing algorithms focus on only one of the two aspects. In this paper, by explicitly modeling discriminative metric learning into the dictionary learning procedure, we propose to formulate robust feature representation learning and discriminative metric learning into a unified framework. To alleviate the issue of amount bias towards hard negative pairs in metric learning, instance selection is conducted for hard negative mining during the similarity constraint formulation. Besides, we come up with closed-form solutions for dictionary and coefficients update. Extensive experiments on three challenging datasets as well as the cross datasets experiments demonstrate the effectiveness and generalization ability of the joint dictionary and metric learning framework.