Dynamic gesture recognition for human robot interaction

In this article a robust and real-time dynamic hand gesture recognition system meant to allow a natural interaction with a service robot, in dynamic environments, is proposed. The main novelty of the proposed approach is the use of temporal statistics about the hand´s positions and velocities as basic information to recognize the gestures. The use of these features allows carrying out the final recognition using a standard Bayes classifier, instead of the traditional Hidden Markov Models. A method for simultaneous gesture segmentation and recognition, which works by finding candidate subsequences that give high scores when matched to a gesture, is proposed. The system uses boosted classifiers to detect hands, and the mean-shift algorithm for their tracking. The system´s performance is validated in a digit recognition system database and in real-world video sequences.