Based on computer vision technology, this paper presents a human motion analysis and target tracking technology based on computer vision. In terms of moving target detection, the current moving target detection technology is summarized, and some experimental results of the algorithm are given. The background difference method under monocular camera is emphatically analyzed. The preliminary human contour is obtained by the background difference method. In order to obtain a smoother target contour, the mathematical morphology is used to remove the noise, and the judgment algorithm of the size of the image connected domain is added. A specific threshold is set to remove the connected domain of the noise block less than the threshold. In the aspect of human motion recognition, this paper selects human motion features, including minimum external rectangle aspect ratio, rectangularity, circularity, and moment invariant. The criteria for selecting human motion features are strong noise resistance and obvious distinction. Then, the three types of human motion images are classified and recognized. After cross-validation and parameter optimization, the recognition accuracy is significantly improved. The experimental results show that the video sequence collected in the field has a total of 376 frames, and the frame rate is 10 frames/s. Due to the small traffic, the mean shift algorithm based on adaptive feature fusion is used to track the target every 2-3 frames. And set the inverse X direction as the direction of entering the scene and the X direction as the direction of moving out of the scene so that the allowable error of the distance between the detection and tracking results is 10. The weight of each feature is dynamically updated by the similarity between the candidate model and the target model, which solves the problem that the mean shift algorithm is not robust enough when similar objects are occluded and interfered and achieves more accurate tracking.