Although human motion form capture is widely used in multiple fields, it often requires a significant amount of time and cost to learn how to operate the device during use. Therefore, this article attempted to apply computer vision (CV) technology and image segmentation algorithms to human motion form capture technology, simplifying the operation scheme and improving recognition accuracy and efficiency. This article provided an in-depth analysis of human motion form capture technology. Firstly, it identified several parts of the current human motion form capture technology that can be optimized, and introduced the effects of these optimized parts on human motion form capture in sports training. This article took the form capture of aerobics athletes as a sample and extracted 50 keyframe images containing aerobics scoring actions from 100 aerobics activity videos. The extraction interval for these keyframe images was at least 10 seconds. Next, this article used histogram equalization to enhance the image, while segmenting and recognizing the human motion forms of the five types of actions in the keyframe images, highlighting the level of action standards of athletes in aerobics. Finally, this article selected 6 key frame images containing different movements of aerobics athletes for comparative experimental analysis. In this experiment, both commonly used optical unlabeled capture techniques and motion morphology capture techniques combining CV and image segmentation algorithms were used to capture the human body in the image. The addition of CV technology and image segmentation has improved the overall performance of human motion morphology capture technology by approximately 26.02%. The integration of CV technology and image segmentation algorithms into human motion form capture technology has greatly improved image processing efficiency. At the same time, CV technology and image segmentation algorithms have also enabled better image processing accuracy in human motion form capture.