| 英文摘要 |
Traditional methods of gathering badminton information involved manually recording simple parameters at the game site or reviewing video footage multiple times. These methods effectively provided the coaching team with insights into the opponent’s competitive habits. However, they were time-consuming, required significant manpower, and were prone to errors due to distractions. In recent years, image motion recognition systems that utilized deep learning, wearable devices, and cameras had gained popularity in badminton information gathering and tactical strategy development. These systems offered a more efficient and accurate alternative to traditional methods. This study systematically reviewed empirical articles written in both Chinese and English from September 2019 to September 2023. The discussion that followed was based on several key considerations: the type of deep learning model suitable for recognizing badminton movement postures, the appropriate extraction method, the database to refer to, the athletes to apply the model to, the number of video events that could be analyzed, the video resolution, the frame ranges, and the types of events (movement postures) that could be observed. The study also evaluated the recognition recall, accuracy, and recognition rates. It further discussed the practical applications of motion recognition systems in badminton teaching, training, and intelligence gathering, and suggested future research directions. The aim was to provide badminton coaches and intelligence collectors with a better understanding of the specific benefits of applying motion recognition systems to badminton competition intelligence collection. The study concluded with practical applications, future research directions, and related recommendations. |
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