RESEARCH FOR PERSON RE-IDENTIFICATION ACROSS MULTIPLE CAMERAS USING MACHINE LEARNING

Abstract

 In recent years, using surveillance cameras installed on streets and commercial facilities, pedestrian safety and customer behavior analysis have been focused on. However, individuals are mostly detected and manually tracked from recorded videos. As labor and costs increase with growing number of cameras, image processing technology that automatically indentify individuals using deep learning have spurred interest. Current technologies mainly focus on reidentifying specific individuals registered in the database system, overlooking unregistered or unknown individuals. Therefore, in this research, we develop the method that uses machine learning to capture the relationship between the features of a person obtained by deep learning, identifies known individuals, and adds unknown individuals to the reference set. This method enables person identification among multiple cameras.