BRISK Local Descriptors for Heavily Occluded Ball Recognition

This paper focuses on the ball detection algorithm that analyzes candidate ball regions to detect the ball. Unfortunately, in the time of goal, the goal-posts (and sometimes also some players) partially occlude the ball or alter its appearance (due to their shadows cast on it). This often makes ineffective the traditional pattern recognition approaches and it forces the system to make the decision about the event based on estimates and not on the basis of the real ball position measurements. To overcome this drawback, this work compares different descriptors of the ball appearance, in particular it investigates on both different well known feature extraction approaches and the recent local descriptors BRISK in a soccer match context. This paper analyzes critical situations in which the ball is heavily occluded in order to measure robustness, accuracy and detection performances. The effectiveness of BRISK compared with other local descriptors is validated by a huge number of experiments on heavily occluded ball examples acquired under realistic conditions