In the visual measurement of rod's angular velocity, the background noise caused by short exposure time leads to serious registration error by mixing and blocking optical flow trajectory. Toaddress this issue,the article proposes a trajectory compensation and noise clipping algorithm. The algorithm takes the difference motionstatistical characteristics between background noise and rod's optical flow trajectory as prior model,and cuts out the background noisetrajectory segments to remove the trajectory aliasing by detecting the speed mutation points through the kernel double sample hypothesis test. By introducing a new optical flow acquisition method which takes the rod hinge fulcrum as the reference, the rod's hinge points of each frame are registered and warpedto the position of the first frame to separate the rod's trajectory from the total composite high-order cycloid to form a low order ideal arc as compensation prior. Secondly, the trajectory is clustered into arcshaped trajectory groups with different radii which fitted by Pratt. Thirdly, the full length trajectory is semi-supervised learned from the v-SVR regression of arc trajectorygroups which acts as a geometric constraint and from x-t dynamic regression to realize compensation. The comparative measurementexperiment of the brush assembly angular velocity and displacement of the needle bed conjugate cam shows that this algorithm can improve the accuracy by 3.26% compared to traditional algorithms such as VBM3D and MeshFlow. The computational complexity is reduced by2 orders. It has broad application prospects in visual fault diagnosis of mechanical rotation motion and digital acquisition of mechanical instruments.