Viewpoint Invariant Person Re-identification with Pose and Weighted Local Features

Abstract

In this study, we propose a viewpoint-invariant person re-identification scheme with pose priors and weighted local features. We divide the pose angle into three classes: (0°, 180°), (45°, 135°), and 90°. Each of the classes has a weighted map. In addition, the texture-based feature, histogram of oriented gradients, is extracted to predict pose angle using support vector machine. Moreover, two additional features, salient color names and local binary patterns (LBP), are extracted. The former feature is computed using a weighted map with Gaussian distribution. The latter feature is computed using a weighted map based on the predicted pose angle. Then, the image representation is concatenated with salient color names and LBP. Finally, we adopt cross-view quadratic discriminant analysis for person re-identification.