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Exploring intelligent image recognition technology of football robot using omnidirectional vision of internet of things

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Abstract

With the improvement and development of intelligent robot technology, such technology is gradually used to improve the intelligence of national sports fitness. The study aims to explore the intelligent image processing methods based on football robots. First, the moving dynamic image is recognized and processed, and computer vision is proposed to simulate animal vision and collect static and dynamic images of football based on the Internet of Things (IoT). Second, a football robot platform is designed to process the collected graphic dataset. Finally, the feature vector and combination matrix of the image in the dataset are calculated, and the obtained feature matrix is input into AdaBoost to obtain the recognition result of football images. The experimental results show that the system error of the auxiliary recognition technology based on IoT is 0.63, and the detection error rate of the auxiliary recognition technology based on AdaBoost is negatively correlated with the number of features. It is concluded that the more training the samples receive, the smaller the detection error rate of the algorithm is. Compared with similar algorithms, the recognition accuracy of the designed algorithm in different datasets is more than 80.1%, and the recognition result is better than that of similar algorithms. Therefore, the algorithm designed and the results obtained prove the feasibility of the proposed intelligent recognition technology in football image recognition. This study provides a reference for the application of artificial intelligence (AI) in the field of physical fitness.

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  1. Gansu University of Political Science and Law, Lanzhou, 730070, Gansu, China

    Tao Wang

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  1. Tao Wang
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Correspondence to Tao Wang.

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Wang, T. Exploring intelligent image recognition technology of football robot using omnidirectional vision of internet of things. J Supercomput 78, 10501–10520 (2022). https://doi.org/10.1007/s11227-022-04314-9

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  • DOI: https://doi.org/10.1007/s11227-022-04314-9

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