Multi-scale saliency features fusion model for person re-identification

Person re-identification mainly uses computer vision technology to determine whether there are specific pedestrians in the image or video. Belong to cross-device retrieval images, due to the changing style of the device led to more difficult person re-identification. The current algorithms use multi-feature fusion methods such as posture detection to match, ignoring the impact of different perspectives, postures and backgrounds on features. This paper proposes a multi-scale feature method based on saliency model. Which uses the salient image extraction algorithm to better filter out the interference of complex background parts, and uses the feature weighting method to fuse the global features and local features to achieve more robust features. Experimental results on three datasets show that the proposed method is superior to the existing method.