Salient Part-Aligned and Keypoint Disentangling Transformer for Person Re-Identification in Aerial Imagery
Junyang Qiu, Zhanxiang Feng, Lei Wang, Jianhuang Lai · 2024
Person re-identification (Re-ID) in aerial imagery aims to retrieve individuals utilizing the UAV surveillance platform. However, the unpredictable changing views of UAVs result in failing to attend consistent foreground regions and achieve semantic alignment. Existing works are limited by coarse-grained part-aligned division and disturbance-sensitive self-attention mechanism. To address these issues, we propose a transformer-based Salient Part-Aligned and Keypoint Disentangling (SPAKD) framework to focus on salient human body regions and align semantic parts with the assistance of keypoints, which consists of a Salient Part-Aware Cross-Attention (SPACA) module and a Keypoint-Assisted Decoder (KAD) module. Specifically, SPACA enhances relationships between salient foreground regions and the whole image, thereby obtaining discriminative part features. KAD employs learnable part prototypes to disentangle human keypoints and aligns decoupled keypoint-assisted features with salient foreground parts based on their affinity information. Extensive experiments demonstrate that our method achieves state-of-the-art performance on aerial-based person Re-ID datasets.