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Human body flexibility fitness test based on image edge detection and feature point extraction

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Abstract

This paper proposes a human flexibility fitness detection algorithm based on edge detection and feature point extraction. This algorithm first improves on the deficiency of the classical Canny operator. Specifically, a hybrid filter is used instead of the original Gaussian filter to improve filtering performance. Next, the templates in the \(45^\circ \) and \(135^\circ \) directions are added based on the original gradient calculation templates, and Otsu algorithm is used to achieve threshold segmentation to obtain the final edge information. Then, based on the obtained edge information, a human body feature point extraction algorithm for calculating the anteflexion angle is proposed, and the feature points of the shoulder, hip, and leg of the person are extracted, and the angle formed by these points is calculated. Size is used to achieve human body flexibility and fitness testing. In order to verify the effectiveness of the edge detection algorithm proposed in this paper, experiments are performed to compare with other algorithms, and the results show that the results of our algorithm are more accurate.

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Acknowledgements

The National Natural Science Foundation of China, Grant No: 61802073; The Scientific and Technological Project of Guangdong Province, China, Grant No.: 2017A040405059; The Key Grant Scientific and Technological Planning Project of Guangzhou, Grant Nos.: 201704020113, 201903010041.

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  1. School of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Xu Lu & Yujing Zhang

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  1. Xu Lu
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  2. Yujing Zhang
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Correspondence to Xu Lu.

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Communicated by A. Di Nola.

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Lu, X., Zhang, Y. Human body flexibility fitness test based on image edge detection and feature point extraction. Soft Comput 24, 8673–8683 (2020). https://doi.org/10.1007/s00500-020-04869-w

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