ANALYSIS OF DIFFERENT DEEP LEARNING APPROACHES BASED ON DEEP NEURAL NETWORKS FOR PERSON RE-IDENTIFICATION

Abstract

In this work, different deep learning approaches based on deep neural networks for person re-identification were analyzed. Both identification and re-identification of people are frequently required in various fields of human life. Some of the most common applications are in various security systems where it is necessary to identify and track a particular person. In the case of person identification, the identity of a particular person needs to be established. In the case of re-identification, the main task is to match the identity of a particular person across different, non-overlapping cameras or even with the same camera at different times. In this work, three different deep neural networks were used for the purpose of person re-identification. Two of them were user-defined, while one of them is a pre-trained neural network adapted to work with a specific dataset. Two neural networks used were Convolutional Neural Networks (CNN). For the defined experiment, it was used own dataset with 13 subjects in gait.

Keywords

• deep neural network (dnn)
• person identification
• person re-identification
• convolutional neural network (cnn)