Two viewpoints based real‐time recognition for hand gestures

Kumar, Amit; Kumar, Abhishek; Guo, Sen

doi:10.1049/iet-ipr.2019.1458

Cited by 16 publications

(3 citation statements)

References 27 publications

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“…Gesture recognition detection was performed on the machine heat applying the system, and the results showed that its accuracy reached more than 90%, which can meet educational needs [17]. Kumar, A. K and other scholars conceptualized a system with low computational time complexity to achieve good gesture recognition accuracy, and comparative experiments pointed out that the recognition rate was better than but camera system, and the simple classifiers used, such as nearest neighbor and decision tree, also have good performance [18]. Scholars such as D. García-Santos discussed the differences between referee gender on technical interventions and stressful situations in basketball, using the IOVAB instrument to calculate the differences between technical interventions and LISEA questionnaires for observation and found that the implementation of an intervention program is necessary to regulate referee anxiety before and after the game [19].…”

Section: Introductionmentioning

confidence: 99%

A real-time basketball referee gesture recognition system based on multi-scale spatio-temporal features

Wang,

HoKun,

Zhou

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, the multi-scale spatio-temporal convolution module and the channel attention mechanism of global information synchronization have excellent performance in the field of sports gesture action recognition; therefore, combining the multi-scale spatio-temporal convolution and the channel attention mechanism to construct the dynamic gesture recognition model. The trained, dynamic gesture recognition model is deployed to cell phones and web pages through Java computational programming language, and the application scenarios of the basketball referee gesture recognition system are explained in detail. The real-time basketball referee gesture recognition system processes are analyzed using simulation analysis after determining the dataset. The basketball referee gesture recognition system’s video cropping frame analysis has an accuracy of 87.64% and 71.25%, with a loss value of 6.24 and 5.59, as shown in the results. When compared to the single-resolution features on the basketball referee gesture recognition system, the fused features exhibit some improvement, and the average recognition rate is 92.20%. This study is applicable to the referee gesture tracking method in complex basketball sports scenarios and lays the foundation for higher semantic level research in the field of sports video analytics, such as action recognition, event detection, and content understanding.

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Section: Introductionmentioning

confidence: 99%

A real-time basketball referee gesture recognition system based on multi-scale spatio-temporal features

Wang,

HoKun,

Zhou

2024

Applied Mathematics and Nonlinear Sciences

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“…With the widespread application and development of digital libraries, the role of facial recognition is becoming increasingly important [ 1 ]. The application of facial length and angle feature recognition technology can improve the intelligent and personalized service quality of libraries [ 2 ]. On a global scale, the construction of digital libraries is no longer just a repository of knowledge, but also a creator and distributor of knowledge, becoming the center of communities and an important place for learning [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Facial length and angle feature recognition for digital libraries

Li,

Ji,

Chen

et al. 2024

PLoS ONE

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With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based on length and angle features of facial expressions, the expression recognition network is explored to improve the recognition rate of different expressions. Finally, an end-to-end network framework based on attention mechanism for facial feature points is constructed to improve the accuracy and stability of facial feature recognition network. To verify the effectiveness of the proposed method, experiments were conducted using the facial expression dataset FER-2013. The experimental results showed that the average recognition rate for the seven common expressions was 97.28% to 99.97%. The highest recognition rate for happiness and surprise was 99.97%, while the relatively low recognition rate for anger, fear, and neutrality was 97.18%. The data has verified that the research method can effectively recognize and distinguish different facial expressions, with high accuracy and robustness. The recognition method based on attention mechanism for facial feature points has effectively optimized the recognition process of facial length and angle features, significantly improving the stability of facial expression recognition, especially in complex environments, providing reliable technical support for digital libraries and other fields. This study aims to promote the development of facial recognition technology in digital libraries, improve the service quality and user experience of digital libraries.

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“…Sharma et al [ 7 ] used a convolutional neural network to recognize images of Indian sign language gestures collected with an RGB camera. Kumar et al [ 11 ] proposed two viewpoint-set-up gesture classification methods. Their experimental results show that compared with a single-camera system, this method has high classification accuracy even when simple classifiers such as nearest neighbors and decision trees are used.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Huo

Qin

et al. 2022

Computational Intelligence and Neuroscience

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As an input method of signal language, the hand movement classification technology has developed into one of the ways of natural human-computer interaction. The surface electromyogram (sEMG) signal contains abundant human movement information and has significant advantages as the input signal of human-computer interaction. However, how to effectively extract components from sEMG signals to improve the accuracy of hand motion classification is a difficult problem. Therefore, this work proposes a novel method based on wavelet packet transform (WPT) and principal component analysis (PCA) to classify six kinds of hand motions. The method applies WPT to decompose the sEMG signal into multiple sub-band signals. To efficiently extract the intrinsic components of the sEMG signal, the classification performance of different wavelet packet basis functions is evaluated. The PCA algorithm is used to reduce the dimension of the feature space composed of the features reflecting hand motions extracted from each sub-band signal. Besides, to ensure higher classification performance while reducing the dimension of the feature space by the PCA algorithm, the classification performance of different dimensions of the feature space is compared. In addition, the effects of the variability of the sEMG signal and the size of the window on the proposed method are further analyzed. The proposed method was tested on the sEMG for Basic Hand Movements Data Set and achieved an average accuracy of 96.03%. Compared with the existing research, the proposed method has better classification performance, which indicates that the research results can be applied to the fields of exoskeleton robot, rehabilitation training, and intelligent prosthesis.

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Two viewpoints based real‐time recognition for hand gestures

Cited by 16 publications

References 27 publications

A real-time basketball referee gesture recognition system based on multi-scale spatio-temporal features

A real-time basketball referee gesture recognition system based on multi-scale spatio-temporal features

Facial length and angle feature recognition for digital libraries

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

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