Computer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.

计算机视觉识别是指使用相机和计算机将计算机视觉（例如目标识别，跟踪，测量和深度图形处理）替换为人眼，以处理图像以使其更适合于人类视觉。针对篮球射击技术与视觉识别运动捕捉技术相结合的问题，本文主要介绍基于计算机视觉识别融合运动捕捉技术的篮球射击技术的研究。本文提出，该技术首先对采集的拍摄视频图像进行背景去除和滤波去噪等预处理操作，以获取视频序列中人物的动作特征，然后使用支持向量机（SVM）和高斯混合模型。获得对象的特征。从样本集中提取部分数据样本，以学习和训练模型。培训完成后，将对其他部分进行分类和识别。动作数据库和实拍视频的仿真测试结果表明，支持向量机（SVM）可以更快，更有效地识别出拍摄视频中出现的动作，平均识别准确率达到95.9％，