Combining AI Techniques for Recognizing Aerobics Sport Videos - Meiling Duan (Applied Bionics and Biomechanics)
In order to improve the recognition accuracy of aerobics athletes’ action features, a multifeature fusion-supported aerobics footprint recognition framework was proposed in this work. By extracting and constructing the 3D peripheral structure reconstruction scheme of the concept of aerobics footprint, we combine the fuzzy feature decomposition method to decompose the aerobics footprint image with multi-pane and suggest fusion. We further realize the similarity identification of aerobics athlete’s trajectory through the wear polymorphic liquefaction method. Simulation results have shown that our proposed method has better effect on the similarity recognition of bodybuilders’ footprints, a higher accuracy of behavior location, a satisfactory notification time, and an accurate recognition of bodybuilders’ footprints. Experiments have shown that our designed system has a small mean square error of aerobics movements and a confirmation of absolute failure. It also has a high recognition fidelity. Noticeably, the reason for the difficulty of aerobics coordination of 10 athletes and the confusion of actual coordination is small, and the accuracy of the Beer effect is high. Our method can be applied for body building, and it can provide the foundation for Game Bill.