Texnicle file basketball1/12/2023 Deep learning techniques such as wearing sensors on human joints to extract skeletal point location information for movement recognition have significant limitations, whereas it is easier and more efficient to recognize human movements in the dynamic video. For example, it could be used to inform professional players, analysts, or basketball coaches about technical movements, and it could also assist referees in judging games on the court. Such practical applications hold great promise for basketball training. If this technology can be used to recognize technical basketball moves as they appear in the video, this would be an important application of deep learning in sport. The convolutional neural network was first applied in the field of images, and after achieving excellent performance in this field, researchers have successively proposed algorithmic models that apply it to video recognition, such as large-scale video classification, multimemory convolutional neural network, and tube convolutional neural network. In addition, there are many other deep learning algorithms that are constantly being developed, such as BP neural network algorithm, back-gradient algorithm, generative adversarial network, transformer, and life cycle assessment. As an example, models based on convolutional neural networks have an important place in deep learning. At the same time, recognition and classification of image data and even video data have become possible with the increase in GPU computing power and the proposal of excellent models in the field of deep learning. With the upgrade of mobile devices and the rise of the video industry, more and more athletes are focusing on using video to analyze their skills during basketball training. At the same time, basketball can be not only a one-on-one game but also a competitive team sport, where the sense of achievement of a beautifully organized attacking score is not dissimilar to that of a great dummy pass. Thus, no matter what physical condition an athlete is in, he or she can find the right position on the court. Smaller players can use their speed to move through the crowd, while bigger players can use their physical strengths to play on the back. In addition, basketball is an all-round exercise for the human body. The equipment is simple, and a single court can accommodate 10–20 people. In China, basketball is one of the most popular sports for young people between the ages of 20 and 35. In recent years, with the awakening of the population's awareness of physical fitness, various sports have been promoted and popularized. The experimental results show that the algorithmic process designed in this study is effective for action recognition on the basketball technical action dataset. To address the above issues, this research proposes a 3D convolutional neural network framework that two different resolution image inputs are performed on the basketball technical action dataset. However, there are many challenges in basketball technical action recognition, mainly including the fact that basketball techniques are numerous and complex. The athletes in the footage in sports videos are relatively fixed, and the scenes are relatively homogeneous, so technical action analysis of basketball technical action videos has certain advantages. In basketball technical action videos, technical action has obvious characteristics. One typical representative is image-based motion recognition technology, which enables video action recognition with a certain degree of feasibility. With the continuous development of computer technology, analysis techniques based on various types of sports data sets are also evolving.
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