Attention-based Shape and Gait Representations Learning for Video-based
  Cloth-Changing Person Re-Identification
Vuong D. Nguyen Samiha Mirza Pranav Mantini Shishir K. Shah
Current state-of-the-art Video-based Person Re-Identification (Re-ID)
primarily relies on appearance features extracted by deep learning models.
These methods are not applicable for long-term analysis in real-world scenarios
where persons have changed clothes, making appearance information unreliable.
In this work, we deal with the practical problem of Video-based Cloth-Changing
Person Re-ID (VCCRe-ID) by proposing "Attention-based Shape and Gait
Representations Learning" (ASGL) for VCCRe-ID. Our ASGL framework improves
Re-ID performance under clothing variations by learning clothing-invariant gait
cues using a Spatial-Temporal Graph Attention Network (ST-GAT). Given the
3D-skeleton-based spatial-temporal graph, our proposed ST-GAT comprises
multi-head attention modules, which are able to enhance the robustness of gait
embeddings under viewpoint changes and occlusions. The ST-GAT amplifies the
important motion ranges and reduces the influence of noisy poses. Then, the
multi-head learning module effectively reserves beneficial local temporal
dynamics of movement. We also boost discriminative power of person
representations by learning body shape cues using a GAT. Experiments on two
large-scale VCCRe-ID datasets demonstrate that our proposed framework
outperforms state-of-the-art methods by 12.2% in rank-1 accuracy and 7.0% in
mAP.