Visual ball tracking and prediction with unique segmented area on soccer robot

Abstract

Object detection and tracking system has been developed by several researchers. This paper present algorithm for visual ball detection and ball estimation for goalie (goalkeeper) robot. The ball is captured by a camera with a fish-eye lens and processed for detection and tracking. Images from fish-eye camera are curved images. Images are thresholded to Hue Saturation Value (HSV). The system can predict goal area and ball position with multilayer backpropagation neural network (BPNN). The BPNN inputs are x and y axis of the ball. The BPNN outputs are goal area prediction and ball area prediction. The training data is unique segmented area. According to the changes of previous ball distance, the system will predict the direction of the next ball position. The achievement result (unique kernel 3×3, MSE <0.001, 30 samples data) for ball position prediction is 76.67%. The achievement result (unique kernel 3×3, MSE <0.001, 30 samples data) for goal area prediction is 100%.