3D Kinematic Analysis of Intelligent Vision Sensor Image in Football Training
With its advantages of high precision, noncontact, and high intelligence, intelligent visual sensor detection technology meets the requirements for online detection of motion status and intelligent recognition of motion images during sports activities, and its applications are becoming more and more...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/40f2d174597f4adc973789e5159342eb |
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| Sumario: | With its advantages of high precision, noncontact, and high intelligence, intelligent visual sensor detection technology meets the requirements for online detection of motion status and intelligent recognition of motion images during sports activities, and its applications are becoming more and more extensive. In order to deeply explore the feasibility of using intelligent vision sensor technology to analyze the three-dimensional action of football, this article uses algorithm analysis method, technology summary method, and physical assembly method, collects samples, analyzes the motion model, streamlines the algorithm, and then creates a model based on intelligent visual sensor technology that can analyze the three-dimensional movement in football training. After the experimental objects are selected, the model is established in the ADM environment. All athletes do a uniform motion, the standard input motion speed is 5 m/s, they all move in the opposite direction relative to their respective coordinate axes, and the motion time is 6 seconds. The results show that the movement curves of the athletes in the three coordinate axis directions are basically the same. When the exercise time is 6 seconds, the coordinate values of the athletes on the three coordinate axes are all 0.992 m. We set six intensities in the experiment: 5%, 15%, 25%, 35%, 45%, and 55%. It can be found that as the noise intensity increases from 5% to 45%, the estimation error gradually increases, but as a whole, it is still at a relatively small level. It shows that the algorithm in this paper still has practical significance. It is basically realized that under the guidance of intelligent vision sensor technology, a model can be designed to successfully and efficiently analyze the three-dimensional movement pattern in training. |
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