Analysis of high-level dance movements under deep learning and internet of things

Wang, Shan; Tong, Shusheng

doi:10.1007/s11227-022-04454-y

Cited by 9 publications

(10 citation statements)

References 22 publications

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“…Different from other published methods, their methods are only composed of fully trainable neural networks and do not rely on any traditional computer graphics methods (Fink et al, 2021). Wang and Tong (2022) further proposed a time consistency method for dynamic pixel loss. Compared with the direct audioto-image method, this cascading method avoids fitting the false correlation between audio-visual signals independent of speech content.…”

Section: Discussionmentioning

confidence: 99%

“…To avoid these pixel jitter problems, they also emphasized the network's attention to audio-visual related areas, and proposed a new attention mechanism with dynamically adjustable pixel-level loss. In addition, to generate clearer images with well synchronized facial motion, they proposed a new regression-based discriminator structure, which considers sequence-level and frame-level information (Wang and Tong, 2022). The above two scholars discussed their methods of generating dance movements from different angles.…”

Section: Discussionmentioning

confidence: 99%

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The use of deep learning technology in dance movement generation

Liu

2022

Front. Neurorobot.

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The dance generated by the traditional music action matching and statistical mapping models is less consistent with the music itself. Moreover, new dance movements cannot be generated. A dance movement generation algorithm based on deep learning is designed to extract the mapping between sound and motion features to solve these problems. First, the sound and motion features are extracted from music and dance videos, and then, the model is built. In addition, a generator module, a discriminator module, and a self-encoder module are added to make the dance movement smoother and consistent with the music. The Pix2PixHD model is used to transform the dance pose sequence into a real version of the dance. Finally, the experiment takes the dance video on the network as the training data and trained 5,000 times. About 80% of the dance data are used as the training set and 20% as the test set. The experimental results show that Train, Valid, and Test values based on the Generator+Discriminator+Autoencoder model are 15.36, 17.19, and 19.12, respectively. The similarity between the generated dance sequence and the real dance sequence is 0.063, which shows that the proposed model can generate a dance more in line with the music. Moreover, the generated dance posture is closer to the real dance posture. The discussion has certain reference value for intelligent dance teaching, game field, cross-modal generation, and exploring the relationship between audio-visual information.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The use of deep learning technology in dance movement generation

Liu

2022

Front. Neurorobot.

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“…After the implementation of the confidence fusion scheme, the final jumping action recognition result from figure skating videos is obtained. [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13] are used for jumping action recognition, and then the true positive rate is calculated according to the action recognition results.…”

Section: Data Setmentioning

confidence: 99%

“…The features are input to improve the deep reinforcement learning network model, and the jump action recognition results are obtained. At the same time, the algorithms in BRMFSJ [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13] are applied to perform action recognition on UCF 101 dataset. According to the recognition accuracy and recall of different recognition algorithms, the PR curve image is drawn, and then the area under the curve is calculated by 11 point interpolation method to obtain the AP value.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…However, the average recognition rate of this algorithm is low. Wang and Tong [16] analyzed the body changes of advanced dance movements based on deep learning algorithm and IoT to promote the practical application of biological image visualization technology. Deep learning and IoT edge computing are used to build the network structure of the dance generation model.…”

Section: Introductionmentioning

confidence: 99%

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Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

Zhou²

2023

ITC

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Figure skating video jumping action is a complex combination action, which is difficult to recognize, and the recognition of jumping action can correct athletes’ technical errors, which is of great significance to improve athletes’ performance. Due to the recognition effect of figure skating video jumping action recognition algorithm is poor, we propose a figure skating video jumping action recognition algorithm using improved deep reinforcement learning in Internet of things (IoT). First, IoT technology is used to collect the figure skating video, the figure skating video target is detected, the human bone point features through the feature extraction network is obtained, and centralized processing is performed to complete the optimization of the extraction results. Second, the shallow STGCN network is improved to the DSTG dense connection network structure, based on which an improved deep reinforcement learning action recognition model is constructed, and the actionrecognition results are output through the deep network structure. Finally, a confidence fusion scheme is established to determine the final jumping action recognition result through the confidence is established. The results show that this paper effectively improves the accuracy of figure skating video jumping action recognition results, and the recognition quality is higher. It can be widely used in the field of figure skating action recognition, to improve the training effect of athletes.

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