Automated Detection and Classification of Soccer Field Objects using YOLOv7 and Computer Vision Techniques

In the last two decades, many technologies have been deployed and utilized in Soccer games (Football) as a result to the huge investment of Federation of International Football Association (FIFA). These technologies aim to monitor and track all soccer match objects including players and the ball itself in order to measure the player performance, and tracking the players’ positions and movements at the field. Latest emerging artificial intelligence and computer vision techniques are being used recently in many systems and deployed in different scenarios. Identifying all field objects automatically has to be the first step in the monitoring process of soccer games. In this paper, we are proposing an automated system that has the ability to detect and track the ball and to detect and classify players and referees on the soccer field. The proposed system implements a detection model using a real-time object detection model YOLOv7 to detect the ball and all humans on the field after building a labeled dataset of 1300 different soccer game frames. It also deploys Improved Color Coherence Vector (ICCV) features to classify all humans on the field to five classes (Team1, Team2, Goalkeeper1, Goalkeeper2, and Referee) using K-Nearest Neighbor algorithm. The proposed system has achieved high accuracy in both the detection and classification modules.

@article{AbuKhait2023,
  title     = {Automated Detection and Classification of Soccer Field Objects using YOLOv7 and Computer Vision Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jafar AbuKhait and Murad Alaqtash and Ahmad Aljaafreh and Waleed Othman},
  doi       = {10.14569/IJACSA.2023.0141191},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141191}
}