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

基于CMAC神经网络和Kalman滤波器的三维视觉跟踪(英文)

In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto 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 thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.

采用Kalman滤波器来预测图像特征点的位置,然后在其周围建立图像处理窗口,以达到减小特征点搜索区域及提高图像处理速度的目的.根据基于图像的视觉伺服IBVS(image BaseD vi-sual servoing)的原理,在视觉伺服控制环中加入CMAC(cerebeuar model aniculation controller)神经网络来实现从图像空间的误差信号向输入空间的控制信号的非线性映射,从而避免了图像雅可比矩阵的不断调整及其复杂的求逆过程.模拟结果表明:采用Kalman滤波器能有效预测特征点的位置,同时采用CMAC神经网络能实现在线有导师学习,末端执行器能较好地对目标物体进行跟踪.