Sparse and Low-Rank Joint Dictionary Learning for Person Re-Identification

In the past decade, the scientific community has become increasingly interested in the re-identification of people. It is still a challenging problem due to its low-quality images; occlusion between objects; and huge changes in lighting, viewpoint and posture (even for the same person). Therefore, we propose a dictionary learning method that divides the appearance characteristics of pedestrians into a shared part, which comprises the similarity between different pedestrians, and a specific part, which reflects unique identity information. In the process of re-identification, by removing the shared part of a pedestrian's visual characteristics and considering the unique part of each person, the ambiguity of the pedestrian's visual characteristics can be reduced. In addition, considering the structural characteristics of the shared dictionary and special dictionary, low-rank, l0 norm and row sparsity constraints instead of their convex-relaxed forms are introduced into the dictionary learning framework to improve its representation and recognition capabilities. Therefore, we adopt the method of alternating directions to solve it. The experimental results of several commonly used datasets show the effectiveness of our proposed method.

Automated summary

In recent years, the scientific community has become increasingly interested in re-identification of people, which remains a challenging problem due to low-quality images, occlusion between objects, and significant variations in lighting, viewpoint, and posture. We propose a dictionary learning method to reduce ambiguity in pedestrian visual characteristics by separating them into shared and specific parts. Experimental results demonstrate the effectiveness of our approach.