Simulation of image feature extraction based on optical sensors in basketball target recognition system

With the continuous advancement of artificial intelligence and computer vision, the application of image recognition-based target tracking systems in the field of sports has become increasingly prevalent. In particular, a basketball goal recognition system holds immense importance for enhancing game fairness and penalty accuracy. Therefore, the objective of this study is to develop a basketball target recognition system that employs optical sensors and utilizes image feature extraction for precise target recognition. To begin with, image data from basketball matches is collected and undergoes preprocessing to ensure optimal image quality. Subsequently, an optical sensor is employed for data acquisition and transmission, enabling the capture of continuous images throughout the basketball game. These images are then subjected to image processing algorithms to analyze and extract key features. Furthermore, machine learning algorithms are implemented to train and optimize the recognition results, thereby enhancing the accuracy and stability of target recognition. By analyzing and processing real-time image data from actual game scenarios, the system successfully extracts the essential image features necessary for precisely recognizing basketball targets. Notably, the test results affirm the system’s exceptional performance in terms of accuracy and real-time target recognition.