Hand Tracking and its Pattern Recognition in a Network of Calibrated Cameras

This thesis presents a vision-based approach for hand gesture recognition which combines both trajectory and hand posture recognition. The hand area is segmented by fixed-range CbCr from cluttered and moving backgrounds, and tracked by Kalman Filter. With the tracking results from two calibrated cameras, the 3D hand motion trajectory can be reconstructed. It is then modeled by dynamic movement primitives (DMP) and a support vector machine (SVM) is trained for trajectory recognition. Scale-invariant feature transform (SIFT) is employed to extract features on segmented hand postures, and a novel strategy for hand posture recognition is proposed. A gesture vector is introduced to recognize hand gesture as a whole which combines the recognition results of motion trajectory and hand postures, where an SVM is trained for gesture recognition based on gesture vectors.