Affiliations: [a]
College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
| [b]
College of Computer and Information, Hohai University, Nanjing, China
| [c]
College of Electronic Information Engineering, Huaiyin Institute of Technology, Huaian, China
Abstract: Person re-identification (ReID) is a critical work in the field of intelligent image processing and deep learning, which has attracted the attention of industry application. Person ReID focuses on matching person images obtained from non-overlapping camera views and finding the person-of-interest. An important unresolved problem is to obtain efficient metric for measuring the similarity among pedestrian images. Lately, deep learning with metric learning has become a general method for person ReID. Yet, previous methods mainly used a variety of distance to measure the similarity among samples. The way of distance measure is more sensitive when the scale changes. In this paper, we propose angular loss with hard sample mining (ALHSM) to learn better similarity metric for the person ReID. Our work uses the angular relationship in triangles as a measure of similarity, minimizing the angle at the negative point of the triangle. ALHSM combines with hard negative mining strategies, which learn better similarity metric and achieve advanced performance on several benchmark datasets. The experimental results show that our work is competitive compared to the state-of-the-art.
Keywords: Person re-identification, deep learning, angular loss, intelligent image processing
