Power dynamics in dance movement therapy

Preda, Rachele

doi:10.1080/17432979.2021.1994010

Cited by 3 publications

(1 citation statement)

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“…Early human posture estimation mainly focused on human contour features or part models. For example, the literature [16,17] designed a human pose estimation algorithm based on part detection by extracting edge force field features through boosting classifier. Literature [18,19], on the other hand, proposed an appearance model combining histogram of oriented gradients (HOG) and color features for human pose estimation.…”

Section: Introductionmentioning

confidence: 99%

Dance Movement Recognition Based on Modified GMM-Based Motion Target Detection Algorithm

Tian¹,

Yang

2022

Security and Communication Networks

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Under the synergistic development of social economy and science and technology, the intelligent teaching of dance has become more and more popular. This teaching method can not only decompose dance movements more specifically, which is easy for students to understand and master, but also get rid of the time and space limitation in traditional dance teaching and provide more independent learning opportunities for students. The problem of low accuracy of dance movement recognition due to complex gesture changes in dance movements is addressed. To this end, this paper proposes a modified motion target detection algorithm based on GMM. The dance movement recognition algorithm first extracts the features of dance movements through a feature pyramid network, then uses a multi-feature fusion module to fuse multiple features to improve the algorithm’s estimation of complex postures, and finally completes the recognition of dance movements. Experiments show that our method can maintain a certain recognition rate in the case where the background and target are easily confused, and can effectively improve the dance action recognition accuracy, thus realizing the action correction function for dancers. This also verifies the effectiveness of the action recognition algorithm for dance movement recognition.

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Section: Introductionmentioning

confidence: 99%