Gesture recognition based on arm tracking for human-robot interaction

In this paper we present a novel approach for hand gesture recognition. The proposed system utilizes upper body part tracking in a 9-dimensional configuration space and two Multi-Layer Perceptron/Radial Basis Function (MLP/RBF) neural network classifiers, one for each arm. Classification is achieved by buffering the trajectory of each arm and feeding it to the MLP Neural Network which is trained to recognize between five gesturing states. The RBF neural network is trained as a predictor for the future gesturing state of the system. By feeding the output of the RBF back to the MLP classifier, we achieve temporal consistency and robustness to the classification results. The proposed approach has been assessed using several video sequences and the results obtained are presented in this paper.