Author(s)

Weirao Wang

Corresponding Author

Weirao Wang

ABSTRACT

Person re-identification aims to judge whether the person images obtained under different cameras correspond to the same person. It can be regarded as a subproblem of image retrieval and has broad application prospects in the fields of intelligent video surveillance, security, and criminal investigation. Person re-identification has become a challenging task in the field of computer vision and attracted more researchers due to the unstable image features affected by low-image resolution, inconsistent shooting angles, poor lighting conditions, continuous changes in background and poses, etc. Existing researches on person re-identification are mainly based on the person images dataset proposes by various institutions, and study learning feature models and measurement methods. Early researches focus on the problem of misalignment and the feature instability caused by the illumination change, trying to build a more stable feature model with stronger discriminative ability. With the deepening of research, more person re-identification datasets expose, and the sample size is constantly expanding. In recent years, with the great success of deep learning algorithm in the field of computer vision, this technology has been introduced into the research of person re-recognition algorithm, which has promoted the improvement of accuracy of person re-identification, bringing new opportunities to the development of person re-identification technology. This paper summarizes the development history, research status and typical algorithms of person re-identification. First, the basic research framework of person re-identification is explained. Then, the research results of the two key technologies (feature expression and similarity metric) of person re-identification are summarized. This paper mainly introduces and analyzes the person re-identification technology based on deep learning algorithm theory. Finally, the representative public datasets in person re-identification are introduced, and compares the performance of the algorithm on the VIPeR dataset. In this paper, based on the JSTL deep feature model, the semi-supervised method is introduced, and the algorithm is improved and analyzed.

KEYWORDS

Person Re-Identification; semi-supervised learning; Deep Learning