Real-Time Monitoring of College Sports Dance Competition Scenes Using Deep Learning Algorithms

In order to improve the real-time detection effect, therefore, a research on real-time scene detection of sports dance competition based on deep learning is proposed. The collected scene image is grayed by using the weighted average method, and the best image interpolation is calculated by using the deep learning method, so as to realize the smooth processing of sawtooth and mosaic information generated by panoramic mapping. After selecting the cube model, the processed scene information is projected to the visual plane to construct the panorama of the competition scene. Finally, combined with the three-frame difference, the changes between adjacent image frames are calculated to obtain the moving target. The test results show that the motion detection accuracy of professional dancers can reach more than 75.0% and that of amateur dancer can reach more than 64.2%.