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SIFT Approach for Ball Recognition in Soccer Images

In this paper a new method for ball recognition in soccer images is proposed. It detects the ball position in each frame but, differently from related previous approaches, it does not require a long and tedious phase to build different positive training sets in order to properly manage the great variance in ball appearance. Moreover it does not need any negative training set, avoiding the difficulties to build it that occur when, as in the soccer context, negative examples abound. A large number of experiments have been carried out on image sequences acquired during real matches of the Italian “Serie A” soccer championship. The reported experiments demonstrate the satisfactory capability of the proposed approach to recognize the ball.