Spatial constrained fine-grained color name for person re-identification

Abstract

Person re-identification is a key technique to match different persons observed in non-overlapping camera views. It’s a challenging problem due to the huge intra-class variations caused by illumination, poses, viewpoints, occlusions and so on. To address these issues, researchers have proposed many visual descriptors. However, these visual features may be unstable in complicated environment. Comparatively, the semantic features can be a good supplement to visual feature descriptors for its robustness. As a kind of representative semantic features, color name is utilized in this paper. The color name is a semantic description of an image and shows good robustness to photometric variations. Traditional color name based methods are limited in discriminative power due to the finite color categories, only 11 or 16 kinds. In this paper, a new fine-grained color name approach based on bag-of-words model is proposed. Moreover, spatial information, with its advantage in strengthening constraints among features in variant environment, is further applied to optimize our method. Extensive experiments conducted on benchmark datasets have shown great superiorities of the proposed method.