Unsupervised Spatial-Temporal Model Based on Region Alignment for Person Re-identification

Abstract

Person re-identification(re-ID) refers to find a specific pedestrian across disjoint camera views. Recently, person re-ID rely on supervised learning to train network by labeled information. Resulting poor generalization in real-world environment because of the lack of pedestrian labels. At the same time, person images are easily affected by background, illumination and pose variations. And these factors make it difficult to extract discriminative features to distinguish different pedestrians. In order to resolve this research problem, we proposing an unsupervised learning alignment method called Region Alignment of Spatial-Temporal Fusion(RASTF) which joints global features with local aligned features to get more discriminative features. Local features are aligned by calculating the shortest distances between regions. Our proposed framework integrates a novel region alignment method in unsupervised network and the experiment results indicate that can outperform the state-of-the-art unsupervised methods.