Person Re-identification in Video Surveillance Systems Using Deep Learning: Analysis of the Existing Methods

• Published in: Automation and remote control Vol. 84; no. 5; pp. 497 - 528
• Main Authors: Chen, H., Ihnatsyeva, S. A., Bohush, R. P., Ablameyko, S. V.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01-05-2023; Springer Nature B.V
• Subjects: Artificial neural networks; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Computer architecture; Computer-Aided Engineering (CAD; Control; Data Analysis; Datasets; Deep learning; Effectiveness; Intellectual Control Systems; Machine learning; Mathematics; Mathematics and Statistics; Mechanical Engineering; Mechatronics; Robotics; Surveillance systems; Systems Theory; Training; accuracy estimation metrics; convolutional neural networks; person re-identification; video data; image descriptors
• ISSN: 0005-1179; 1608-3032
• DOI: 10.1134/S0005117923050041

This paper is devoted to a multifaceted analysis of person re-identification (ReID) in video surveillance systems and modern solution methods using deep learning. The general principles and application of convolutional neural networks for this problem are considered. A classification of person ReID systems is proposed. The existing datasets for training deep neural architectures are studied and approaches to increasing the number of images in databases are described. Approaches to forming human image features are considered. The backbone models of convolutional neural network architectures used for person ReID are analyzed and their modifications as well as training methods are presented. The effectiveness of person ReID is examined on different datasets. Finally, the effectiveness of the existing approaches is estimated in different metrics and the corresponding results are given.