Circle detection problems have been largely studied in the last years for numerous image processing applications. Automatic ball recognition in television sequences of soccer images is a fundamental task to solve: a number of doubtful cases occurs during the game especially for detecting the outside event and the goal event. This domain is challenging as a great number of problems have to be managed, such as occlusions, shadows, objects similar to the ball, real time processing. In this work we have developed a visual framework that tries to solve the above problems mainly considering the changes of light conditions that modify the appearance of the ball during the matches. The ball detection algorithm has to be very simple in terms of time processing but also efficient in terms of false positive rate. The framework we propose consists of two sequential steps for solving the ball recognition problem: the first step uses a template matching algorithm to detect the region of the image that is the best candidate to contain an object whose shape is similar to the ball; in the second step, a neural classifier is applied on the selected region to confirm if the ball has been properly detected or a false positive has been found.
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