Video analysis method of basketball training assistant based on deep learning theory during COVID-19 spread

Deep Learning Algorithm-Based Target Detection and Fine Localization of Technical Features in Basketball

Based on SSD to detect players, a super-pixel-based FCN-CNN player segmentation algorithm is proposed to filter out the complex background around players, which is more conducive to the subsequent pose estimation for target detection and fine localization of basketball technical features. The high resolution capability of CNN is used to extract images and perform computational preprocessing to identify typical basketball sports actions in video streams-rebounds, shots, and passes-with an accuracy rate of up to 95.6%. By comparing with three classical classification algorithms, the results prove that the target detection system proposed in this study is effective for target detection and fine localization of basketball sports technical features.

Internet of Things and Edge Computing Model Optimization in Physical Training Status and Countermeasures in College Sports Basketball Optional Course

In recent years, people have paid special attention to their own health status and paid more and more attention to the cultivation of physical fitness. People of different ages have begun physical exercises. As a compulsory course in university, physical education has played a very positive role in the health of students. In order to promote the common development of basketball activities in many sports academies across the country and improve the educational quality of basketball majors, with a view to cultivating basketball and football talents in line with the culture and social development of the times, this article investigates and evaluates the basketball activities of sports coaches in many sports academies, discusses the pros and cons of high school sports in basketball and other related fields, and provides references and suggestions for improvement in the reform of basketball activities in some sports academies. The results of the study found that based on the Internet of Things and edge computing models, physical training in college sports basketball optional courses has been greatly improved compared with traditional physical training methods. Adding adaptive scheduling to Core 1, priority 1 and 5 was distributed on Core 2∼5. Priority 2 tasks tend to be more balanced.