A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

Shen, Dianhuai; Jiang, Xueying; Teng, Lin

doi:10.4108/eai.17-12-2021.172439

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…We analyze the positioning nodes and extract the feature points of each joint of athletes' limbs in track and field by using equation (2). ) , , , (3) Affine deformation of human body is easy to occur in track and field sports, which leads to the appearance of action feature points with low brightness and shade.…”

Section: Principle Of Athlete Posture Recognition and Correctionmentioning

confidence: 99%

“…In the World Track and Field Championships, coaches and athletes of all countries attach great importance to competition results. And each time the progress of competition results are inseparable from the improvement of athletes' skills [1,2]. Correct track and field posture is helpful for athletes to improve their skills, while wrong track and field posture is not conducive to the progress of athletes' skills and can not achieve ideal results.…”

Section: Introductionmentioning

confidence: 99%

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A novel SURF-RANSAC matching method for athletics posture recognition

Duan¹

2018

ICST Transactions on Scalable Information Systems

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In athletics sports, accurate identification and correction of athlete's wrong posture can improve the quality of athlete's daily training. In the course of athletics sports, affine deformation of human body is easy to occur, which leads to the appearance of action feature points with low brightness and shading. However, the traditional method is to extract these feature points and compare them with the correct posture to realize the recognition and correction of posture, which leads to the failure of real-time detection and correction of athletes' wrong posture. Therefore, this paper proposes a method of posture recognition and correction for athletes with depth image bone tracking. The threshold method is used to preprocess the image, and the Kalman filter is used to filter the acquired image. The motion feature points are obtained from the filtered image by Gaussian distribution function. By improving SURF-RANSAC method, marginal points and action feature points with low brightness are screened out. Euclidean distance method is used to determine the distance between two adjacent feature points, and feedback monitoring principle is used to identify and correct the wrong posture. The simulation results show that the improved posture recognition and correction method of depth image bone tracking can realize tracking and monitoring of track and field athletes' movements, complete the detection and recognition of track and field sports posture with high accuracy and strong stability.

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Section: Principle Of Athlete Posture Recognition and Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel SURF-RANSAC matching method for athletics posture recognition

Duan¹

2018

ICST Transactions on Scalable Information Systems

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“…It has important research value in monitoring system, intelligent home and virtual reality. Different from simple image recognition, human motion recognition is affected by many factors, such as chaotic background, different image acquisition equipment, insufficient categories of human motion database and so on [1,2]. At present, there are mainly traditional machine learning and deep learning methods for human motion recognition.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction of dance movement based on deep learning and deformable part model

Gao¹,

Wang²

2018

ICST Transactions on Scalable Information Systems

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In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set.

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“…Through the recognition of human expression, posture or gesture to understand the human intention, so that the machine can recognize the human intention and make response to achieve the purpose of interaction [2,3].…”

Section: Introductionmentioning

confidence: 99%

Basketball posture recognition based on HOG feature extraction and convolutional neural network

Gao¹

2018

ICST Transactions on Scalable Information Systems

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Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical treatment, sports, security and other aspects. With the development of machine learning, the application value of basketball pose recognition in physical education is becoming more and more extensive. This paper constructs a novel convolutional neural network model to recognize basketball posture. The model consists of 11 layers. Convolution and pooling operations are carried out for five basketball postures in the sampled data set. By fusing with the features extracted from HOG, finer features can be obtained. Finally, the data set is trained and recognized by entering the full connection layer for classification. The results show that compared with the traditional machine learning methods, the recognition performance of new model is better.

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A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

Cited by 9 publications

References 26 publications

A novel SURF-RANSAC matching method for athletics posture recognition

A novel SURF-RANSAC matching method for athletics posture recognition

Feature extraction of dance movement based on deep learning and deformable part model

Basketball posture recognition based on HOG feature extraction and convolutional neural network

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