Multilevel triplet deep learning model for person re-identification

Person re-identification (Re-ID) is a typical computer vision problem which matches pedestrians from different cameras. It remains challenging to cope with the variation in light, the change of human pose and view point difference. Many existing person re-identification methods may have difficulty in matching pedestrians when their pictures are similar in appearance or there is object occlusion in pictures. The main problem with these existing methods is that the detail and global features of the images are not well combined. In this paper, we improved the performance of deep CNN network with the proposed Multilevel feature extraction strategy and built a novel Multilevel triplet deep learning model corresponding to our method. The Multilevel feature extraction strategy focuses on combining fine, shallow layer information with coarse, deeper layer information by extracting fusion feature maps from different layers for a better representation of pedestrians. The Multilevel triplet deep learning model (MT-net) provides an end-to-end training and testing plain for our feature extraction strategy. The experiment on the benchmark datasets validated that our multilevel triplet deep learning model had better performance comparing with many state-of-the-art person re-identification methods.