Research of Deep Learning-Based Multi Object Classification and Tracking for Intelligent Manager System

Abstract

Recently, intelligent control systems are developing rapidly in various application fields, and methods for utilizing technologies such as deep learning, IoT, and cloud computing for intelligent control systems are being studied. An important technology in an intelligent control system is recognizing and tracking objects in images. However, existing multi-object tracking technology has problems in accuracy and speed. In this paper, a real-time intelligent control system was implemented using YOLO v5 and YOLO v6 based on a one-shot architecture that increases the accuracy of object tracking and enables fast and accurate tracking even when objects overlap each other or when there are many objects belonging to the same class. The experiment was evaluated by comparing YOLO v5 and YOLO v6. As a result of the experiment, the YOLO v6 model shows performance suitable for the intelligent control system.