Abstract
In view of the increase in the number of people participating in dance rating assessments, this paper proposes a dance assessment technology based on human body posture recognition. This technique adopts the human target detection of the dance video, extracts bone key points, and then uses the video data set collected by professional dancers to conduct PoseC3D model training, enabling the model to classify the basic movements of the dance; then, the dynamic time normalization algorithm is used to evaluate the classified movements. The experimental results show that this technology can accurately identify the basic movements of various dances and accurately give the evaluation score of the corresponding movements, thus reducing the work intensity of the assessment staff.
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Li, Y., Zhu, Y., Wang, Y., Gao, Y. (2023). Research on Dance Evaluation Technology Based on Human Posture Recognition. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_7
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DOI: https://doi.org/10.1007/978-981-99-5968-6_7
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