3-D visual tracking based on CMAC neural network and Kalman filter

The Kalman filter is used to predict image feature position around which an image-processing window is then established to diminish feature-searching area and to heighten the image-processing speed. According to the fundamentals of image-based visual servoing (IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into the visual servo control loop to implement the nonlinear mapping from the error signal in the image space to the control signal in the input space instead of the iterative adjustment and complicated inverse solution of the image Jacobian. Simulation results show that the feature point can be predicted efficiently using the Kalman filter and on-line supervised learning can be realized using CMAC neural network; end-effector can track the target object very well.