People Re-identification Based on Bags of Semantic Features

People re-identification has attracted a lot of attention recently. As an important part in disjoint cameras based surveillance system, it faces many problems. Various factors like illumination condition, viewpoint of cameras and occlusion make people re-identification a difficult task. In this paper, we exploit the performance of bags of semantic features on people re-identification. Semantic features are mid-level features that can be directly described by words, such as hair length, skin tone, race, clothes colors and so on. Although semantic features are not as discriminative as local features used in existing methods, they are more invariant. Therefore, good performance on people re-identification can be expected by combining a set of semantic features. Experiments are carried out on VIPeR dataset. Comparison with some state-of-the-art works is provided and the proposed method shows better performance.