Ball detection in static images with Support Vector Machines for classification
We present a general method for detecting balls in images at the aim of automatically detecting goals during a soccer match. The detector learns the object to detect by using a supervised learning scheme called Support Vector Machines, in which the examples are views of the object. Due to the attitude of the camera with respect to football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a soccer match. Numerous theoretical and practical issues are addressed in the paper. The first one concerns the determination of negative examples relevant for the problem at hand and the training of a reference classifier in the case of an unbalanced number of positive and negative examples. The second one focuses on the reduction of the computational complexity of the reference classifier during the test phase, without increasing its generalization error. The third issue regards the problem of parameter selection, which is equivalent, in our context, to the problem of selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. Experimental results on real images show the performances of the proposed detection scheme.
support vector machines classification object detection