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• W4282972595 abstract "Understanding the underlying semantics of performing arts like dance is a challenging task. Analysis of dance is useful to preserve cultural heritage, make video recommendation systems, and build tutoring systems. To create such a dance analysis application, three aspects of dance analysis must be addressed: (1) segment the dance video to find representative action elements, (2) recognize the detected action elements, and (3) recognize sequences formed by combining action elements according to specific rules. This paper attempts to address the three fundamental problems of dance analysis raised above, with a focus on Indian Classical Dance, em Bharatanatyam. Since dance is driven by music, we use both musical and motion information to extract action elements. The action elements are then recognized using machine learning and deep learning techniques. Finally, the Hidden Markov Model (HMM) and Long Short-Term Memory (LSTM) are used to recognize the dance sequence." @default.
• W4282972595 created "2022-06-17" @default.
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• W4282972595 date "2022-08-01" @default.
• W4282972595 modified "2023-10-18" @default.
• W4282972595 title "Posture and sequence recognition for Bharatanatyam dance performances using machine learning approaches" @default.
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• W4282972595 doi "https://doi.org/10.1016/j.jvcir.2022.103548" @default.
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