Behavior Recognition and Monitoring System for Office Workers Using Deep Learning and Two Surveillance Cameras

抄録

In many offices, starting and finishing times of workers are recorded as part of attendance control, but they do not monitor the employees' behaviors during the workday whether they are taking appropriate breaks. In this paper, we propose a behavior recognition system using the object detection algorithm YOLOv5 and the object tracking algorithm DeepSORT and two surveillance cameras. In addition, we propose a person matching method between the images from two surveillance cameras using projective transformation. We will show that the recognition works substantially well by integrating the recognition results by using two cameras.