Abstract:
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.