RETRACTED: Action recognition of dance video learning based on embedded system and computer vision image

Fu-bo, MA

doi:10.1016/j.micpro.2020.103779

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…Two works [18], [19] try to recognize the dance video actions based on postures. These are hardware-based embedded systems that use Field Programmable Gate Array (FPGA).…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…As discussed, there are several recent works [5]- [7], [18]- [23] done on motion/action recognition in ICDs, but except [5] and [6], the rests are based on pose/posture action recognition. Though [5] and [6] try to solve the motion recognition.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…To drop the excess frames for the motion samples with more than 16 frames, we formulate an equation as reported in (3). For example: If there exist 20 frames in a given motion then we consider the frame indices (F rame i ) [1,2,3,5,6,7,8,10,11,12,13,15,16,17,18,20]…”

Section: Cnn As Classifiermentioning

confidence: 99%

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Motion Recognition in Bharatanatyam Dance

et al. 2022

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This paper provides a method to understand the underlying semantics of Bharatnatyam dance motion and classifies it. Each dance performance is audio-driven and spans over space and time. The dance is captured and analyzed, which is helpful in cultural heritage preservation, and tutoring systems to assist the naive learner. This paper attempts to solve the fundamental problem; recognizing the motions during a dance performance based on motion-pattern. The used dataset is the video recordings of an Indian Classical Dance form known as Bharatanatyam. The different Adavus (The basic unit of Bharatanatyam) of Bharatanatyam dance are captured using Kinect. We choose RGB from various forms of captured data (RGB, Depth, and Skeleton). Motion History Image (MHI) and Histogram of Gradient of MHI (HoGMHI) are computed for each motion and used as an input for the Machine Learning (ML) algorithms to recognize motion. The paper explores two ML techniques; Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The overall accuracy of both the classifiers is more than 90%. The novelties of the work are (a) analysing all possible involved motions based on the motion-patterns rather than the joint velocities or pose, (b)exploring the impact of training data and the different features on the classifiers' accuracy, (c) not restricting the number of frames in a motion during recognition and formulate a method to deal with the variable number of frames in the motions.

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“…Two works [18], [19] try to recognize the dance video actions based on postures. These are hardware-based embedded systems that use Field Programmable Gate Array (FPGA).…”

Section: Motivation and Related Workmentioning

confidence: 99%

Section: Motivation and Related Workmentioning

confidence: 99%

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Motion Recognition in Bharatanatyam Dance

et al. 2022

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“…Arti cial Intelligence (AI), which refers to the intelligence displayed by programs written by humans, etc., is the technology and science of studying and discovering human intelligence [14][15][16]. Arti cial intelligence technologies are now widely used in various production activities, and the most widely used elds mainly include image processing, natural language processing, and human speech and semantic recognition.…”

Section: Concepts Related To Artificial Intelligence Dance Movement R...mentioning

confidence: 99%

Research on Multimodal Dance Movement Recognition Based on Artificial Intelligence Image Technology

Zeng

2022

Computational Intelligence and Neuroscience

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At present, most robot dances are precompiled. Changing music requires manual adjustment of relevant parameters and metamovements, which greatly reduces the fun and intelligence. In view of the above problems, this paper designed CNN system, studied the multimodal dance movement recognition algorithm of artificial intelligence image technology, and completed the construction of a multimodal dance movement calculation system example. The results show that the CNN algorithm and the Winograd algorithm-based coprocessor-optimized CNN network in multimodal dance movement recognition with image technology reduce from a maximum of 132s to 26s in the runtime criterion, with a maximum reduction of 80%; from a maximum of 73.5% to 16.2% in the memory access criterion, with a maximum reduction of 57.3%; and from a maximum of 93.6% to 25.2% in the power consumption ratio criterion, with a maximum reduction of 68.4%. In the power consumption ratio criterion, the maximum reduction from 93.6% to 25.2% is 68.4%. The maximum accuracy of the proposed optimization method is 95.1%. The solution is proposed to address the problem of insufficient performance of traditional dance movement recognition, which will contribute to the development of artificial intelligence and dance industry.

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“…In dt time, the point moves to a new position (x + dx , y + dy) in the next frame. At this time, because the time is very short, the luminance of this point has the relationship [22,23] as shown in the following formula: I(x, y, t) I(x + dx, y + dy, t + dt). (10) e Taylor expansion formula is shown in the following formula:…”

Section: Dance Action Recognition Based On DL Networkmentioning

confidence: 99%

Research on Automatic Dance Generation System Based on Deep Learning

Lan¹

2022

Mathematical Problems in Engineering

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How to model the mapping relationship between music and dance so that the model can automatically generate the corresponding dance based on the rhythm and style of music is the most important issue to be resolved in the automatic arrangement of music and dance. Due to the flexible movement of limbs and the change in camera angle, the visual information of various parts of the human body changes drastically in practical applications, posing significant difficulties in the generation of high-resolution target dance images. Based on deep learning, this paper proposes a two-way voluntary neural network (TWCNN) dance automatic generation system (DL). The long short-term memory (LSTM) network model is an action generation sub-network, which generates each frame of action based on the extracted high-level music features and the bottom-level action features from the previous moment. The recognition accuracy of the proposed algorithm is 12.4 percent and 6.8 percent better than that of the conventional depth CNN, according to the test results.

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RETRACTED: Action recognition of dance video learning based on embedded system and computer vision image

Cited by 10 publications

References 4 publications

Motion Recognition in Bharatanatyam Dance

Motion Recognition in Bharatanatyam Dance

Research on Multimodal Dance Movement Recognition Based on Artificial Intelligence Image Technology

Research on Automatic Dance Generation System Based on Deep Learning

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