Multi-scale Salient Features for Person Re-identification

Abstract

Person re-identification is a technology that uses computer vision to match and retrieve the same target person across cameras, which has a wide range of applications in the fields of intelligent video surveillance, intelligent security, and unmanned driving. Aiming at the problem of low accuracy caused by complex background information, occlusion and other factors in person re-identification, the extracted features are not discriminative and robust, resulting in low accuracy, a person re-identification method based on multi-scale saliency features is proposed. Firstly, the saliency detection is used to extract the distinctive feature areas with discriminative power in person, so that the features of distinguishing person inages are more prominent. Secondly, these saliency features are merged with global features and the fused features are divided into different scales, and the global features are divided into three different scales to enhance the expression of features. Finally, according to the difference of global and local features, three kinds of loss functions are combined for learning. In the inference stage, the global feature and the local feature are fused into a new feature vector for metric learning. The proposed method has been used in the person re-identification public datasets Market1501 and DukeMTMC-reID to do a lot of effective verification experiments. The experimental results show that the features extracted by the proposed method have strong distinguishability and robustness.