Robust Representations for Person Re-identification

Abstract

Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in reality. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this chapter. First, we propose a cross-view projective dictionary learning (CPDL) approach, which learns effective features for persons across different views. CPDL is a general framework for multi-view dictionary learning. Secondly, by utilizing the CPDL framework, we design two objectives to learn low-dimensional representations for each pedestrian in the patch-level and the image-level, respectively. The proposed objectives can capture the intrinsic relationships of different representation coefficients in various settings. We devise efficient optimization algorithms to solve the objectives. Finally, a fusion strategy is utilized to generate the similarity scores. Experiments on the public VIPeR, CUHK Campus and GRID datasets show that our approach achieves the state-of-the-art performance.