Abstract
Hand gestures, either static or dynamic, for human computer interaction in real time systems is an area of active research and with many possible applications. However, vision-based hand gesture interfaces for real-time applications require fast and extremely robust hand detection, and gesture recognition. Attempting to recognize gestures performed by officials in typical sports video places tremendous computational requirements on the image segmentation techniques. Here we propose an image segmentation technique based on the Histogram of Oriented Gradients (HOG) features that allows recognizing the signals of the basketball referee from videos. We achieve an accuracy of 97.5% using Support Vector Machine (SVM) for classification.
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