An Orientation-Aware Attention Network for Person Re-Identification

Abstract

Humans always identify persons through their characteristics, salient attributes, and these attributes’ locations on the body. Most person re-identification methods focus on global and local features corresponding to the former two discriminations, cropping person images into horizontal strips to obtain coarse locations of body parts. However, discriminative clues corresponding to location differences cannot be discovered, so persons with similar appearances are often confused because of their alike components. To address the above problem, we introduce pixel-wise relative positions for the invariance of their orientations in viewpoint changes. To cope with the scale change of relative position, we combine relative positions with self-attention modules that perform on multi-level features. Moreover, in the data augmentation stage, mirrored images are given new labels due to the conversion of the relative position along a horizontal orientation and change in visual chirality. Extensive experiments on four challenging benchmarks demonstrate that the proposed approach shows its superiority and effectiveness in discovering discriminating features.

Keywords

• person re-identification
• orientation-aware attention
• visual chirality