Aiming at the problems of slow feature point extraction, high mismatch rate, and multi-channel video image stabilization of multi-camera cameras that are too time-consuming and poor real-time performance in current image stabilization algorithms based on feature point matching, this paper proposes an improved Star feature point fast extraction algorithm. First, the Star algorithm is used to extract the feature points of the current frame of the video, and then the optical flow method is combined with the feature point information of the current frame to track and predict the feature points corresponding to the next frame, which speeds up the extraction of feature points and reduces the number of invalid feature points extracted. The inter-frame motion vector is obtained by obtaining the corresponding affine transformation matrix for the matched feature points, and then the Kalman filter is used to filter the inter-frame motion vector to filter out the random camera shake vector and retain the subjective motion of the camera vector. Finally, perform image compensation according to the filtered motion vector to obtain a stable image sequence. In view of the multi-channel video jitter situation in the seeker, this article is based on the OpenMP parallel development library and uses the advantages of multi-core processors to achieve parallel real-time image stabilization of multi-channel jitter videos. The results of parallel image stabilization of 4 dithered videos (BlurCar series videos of OTB100, resolution 640×480, frame rate 30 FPS) under the PC platform of this algorithm are: PSNR increased by 4.62 dB on average, and single frame time consumption average is 14.51 ms. Experiments have confirmed that the algorithm in this paper has high computational efficiency and accuracy, and can realize real-time image stabilization of multiple input videos. Compared with other image stabilization algorithms, it has better practical value.