Continuous Gesture Trajectory Recognition System Based on Computer Vision

In this paper, we propose an automatic system for recognizing continuous gestures in real-time, including Arabic numbers (0-9) and alphabets (A-Z). We present an improved method for hand area detection and segmentation based on YCbCr and HSI mixed skin color space, the improved CAMSHIFT algorithm used for hand tracking. Orientation dynamic features are obtained from spatiotemporal trajectories and then quantized to generate its code words. An improved HMM-FNN model is proposed for gesture recognition based on the code words, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network for fuzzy rule modeling and fuzzy inference. The algorithm we presented has better performance and achieves average recognition rate 95.76% and 93.64% for Arabic Numbers and Alphabets, respectively.