Abstract
The recognition technology using conventional sensors or image processing is effective for static gesture recognition, but for dynamic gesture recognition, the moving object can not be tracked in time, resulting in low recognition accuracy and efficiency. In order to optimize the above problems, the dynamic recognition method of track and field posture based on mobile monitoring technology is studied. Set up mobile monitoring equipment in the movement area and the movement track to acquire the data of track and field movement posture. After de-noising the track and field posture data, a Gaussian model is established to segment the image background. Based on the human skeleton model, the motion posture features are extracted. Using BP neural network improved by artificial fish swarm to classify the input movement posture data, the recognition of track and field movement posture is realized. The test results show that the recognition accuracy of the proposed methods is higher than 95%, the recognition efficiency is greatly improved, and it has good practical value.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lin, Q., Han, L. (2023). Dynamic Recognition Method of Track and Field Posture Based on Mobile Monitoring Technology. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-28867-8_25
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DOI: https://doi.org/10.1007/978-3-031-28867-8_25
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