Improving Badminton Player Detection Using YOLOv3 with Different Training Heuristic

Abstract

There has been a considerable rise in the amount of research and development focused on computer vision over the previous two decades. One of the most critical processes in computer vision is "visual tracking," which involves following objects with a camera. Tracking objects is the practice of following an individual moving object or group of moving things over time. Identifying or connecting target elements in consecutive video frames of a badminton match requires visual object tracking. The aim of this study is to identify badminton players using the You Only Look Once (YOLO) technique in conjunction with a variety of training heuristics. This methodology has a few advantages over other approaches to detecting objects. The convolutional neural network and Fast convolutional neural network are two examples of the many algorithmic approaches that are available. In this study, a neural network is used to produce predictions about the bounding boxes and the class probabilities for these boxes.. The results demonstrated that it was far faster than other methods in terms of its ability to recognize the image. The performance of image classification networks significantly improved as a result of the implementation of a variety of training strategies for the detection of objects. The mean average precision score for YOLOv3 with various training heuristics increased from 32.0 to 36.0 as a direct result of these adjustments. In comparison to YOLOv3, our future study might examine the performance of alternative models like Faster R-CNN or RetinaNet.