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, 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.
Highlights
Pedestrian re-identification aims to identify specific pedestrians through cameras at different locations, that is, to establish the correspondence between people at different visual ranges
As high-dimensional visual features usually do not capture the invariant factors under sample variance, a distance metric is introduced into pedestrian re-identification
Since sparse dictionary learning is a special case of metric learning, it has been successfully applied in computer vision fields such as face recognition [19,20], and is applied to pedestrian re-recognition
Summary
Pedestrian re-identification (re-ID) aims to identify specific pedestrians through cameras at different locations, that is, to establish the correspondence between people at different visual ranges. Li et al [31] proposed a person re-ID method to divide a pedestrian’s appearance features into different components They developed a framework for learning a pair of commonality and specificity dictionaries, while introducing a distance constraint to force the particularities of the same person over the specificity dictionary to have the same coding coefficients and the coding coefficients of different pedestrians to a have weak correlation. The large number of cameras in large datasets increases the pose differences and motion differences of the same pedestrian These all limit the role of spatiotemporal features in pedestrian re-identification. It this paper, we propose a new special and shared dictionary learning model with structure characteristic constraints, which has stronger interpretability.
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