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

Jun Sun, Lingchen Kong and Biao Qu
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Jun Sun: Department of Applied Mathematics, Beijing Jiaotong University, Beijing 100044, China
Lingchen Kong: Department of Applied Mathematics, Beijing Jiaotong University, Beijing 100044, China
Biao Qu: Institute of Operations Research, Qufu Normal University, Rizhao 276826, China

Mathematics, 2022, vol. 10, issue 3, 1-16

Abstract: 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, l 0 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.

Keywords: re-identification; dictionary learning; sparsity constraints; low rank constraints (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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