Abstract
Recognition of hand gestures (hand signals) is an active research area for human computer interaction with many possible applications. Automatic machine vision-based hand gesture interfaces for real-time applications require fast and extremely robust human, pose and hand detection, and gesture recognition. Attempting to recognize gestures performed by official referees in sports (such as basketball game) video places tough requirements on the image segmentation techniques. Here we propose an image segmentation technique based on the histogram of oriented gradients and local binary pattern (LBP) features, which allow recognizing the signals of basketball referee from recorded game videos and achieved an accuracy of 95.6% using LBP features and support vector machine for classification. Our results are relevant for real-time analysis of basketball game.
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