Implementation of perspective-n-point techniques and YOLOv5 algorithm based on surveillance camera for localization

Abstract

The technology of processors has advanced significantly, resulting in smaller and more powerful devices with much processing capability. Particularly, camera technology has witnessed extensive research in utilizing images for various applications. Currently, surveillance cameras are widely used for security purposes when abnormal events occur. In this research, the benefits of utilizing data from surveillance cameras are explored to assist in determining the position of a moving robot using the perspective-n-point (PnP) technique. the scale factor, which varies, has been improved by integrating checks with the YOLOv5 algorithm. This algorithm employs a custom model to specifically detect the robot of interest, enabling the determination of its real-world position using multiple surveillance cameras. These cameras have different perspectives within the same area. Considering the deviation caused by determining the position from a single viewpoint, multiple cameras are employed to mitigate this issue.