Human Motion Gesture Recognition Based on Computer Vision

Ma, Rui; Zhang, Zhendong; Chen, Enqing

doi:10.1155/2021/6679746

Cited by 16 publications

(5 citation statements)

References 30 publications

(37 reference statements)

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“…Wenbo et al put forward a protocol recognition method based on CNNs to improve the accuracy of feature acquisition, and it was proved that the proposed algorithm had high accuracy and fast convergence speed [14]. Ma et al proposed a novel deep convolutional generative adversarial network to recognize human action posture, and verified through experiments that its model had significant advantages over existing models [15]. Chen et al put forward a multi-radar collaborative HAR model based on transfer and integrated learning to solve the view limitation in AR.…”

Section: Related Workmentioning

confidence: 99%

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Liu

2023

IJACSA

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An action recognition model based on 3D skeleton data may experience a decrease in recognition accuracy when facing complex backgrounds, and it is easy to overlook the local connection between dynamic gradient information and dynamic actions, resulting in a decrease in the fault tolerance of the constructed model. To achieve accurate and fast capture of human skeletal movements, a directed graph convolutional network recognition model that integrates attention mechanism and convolutional neural network is proposed. By combining spacetime converter and central differential graph convolution, a corresponding central differential converter graph convolutional network model is constructed to obtain dynamic gradient information in actions and calculate local connections between dynamic actions. The research outcomes express that the crosstarget benchmark recognition rate of the directed graph convolutional network recognition model is 92.3%, and the crossview benchmark recognition rate is 97.3%. The accuracy of Top-1 is 37.6%, and the accuracy of Top-5 is 60.5%. The cross-target recognition rate of the central differential converter graph convolutional network model is 92.9%, and the cross-view benchmark recognition rate is 97.5%. Undercross-target and cross-view benchmarks, the average recognition accuracy for similar actions is 81.3% and 88.9%, respectively. The accuracy of the entire action recognition model in single-person multi-person action recognition experiments is 95.0%. The outcomes denote that the model constructed by the research institute has higher recognition rate and more stable performance compared to existing neural network recognition models, and has certain research value.

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Section: Related Workmentioning

confidence: 99%

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Liu

2023

IJACSA

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“…Ma et al [ 20 ] suggested a novel deep convolutional generation confrontation network to recognize human motion poses. This method uses a deep convolutional stacked hourglass network to precisely extract the location of key joint points on the image.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning

Alhakbani,

Alghamdi,

Al-Nafjan

2023

Sensors

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Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate.

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“…Various techniques have been developed for activity recognition, both computer vision-based and sensor-based [2]. Computer vision usage requires a camera device to capture human activities [3,4]. This computer vision technique can provide good results, but lighting, privacy, processing complexity, and fixed camera positions still need to be solved [1,2,6].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Human Activity Recognition (HAR) Using A Single Tri-axial Accelerometer Based on a Combination of Three Feature Subsets

2024

IJIES

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Human Activity Recognition (HAR) is focused on Activities of Daily Living and developed in the health and human security fields. The HAR concept was introduced in previous research using multi-sensor devices. In their implementation, wearable devices require computational and real-time environmental limitations. This paper proposed a new approach for HAR using a machine learning-based single-sensor accelerometer. This research aimed to determine the performance of machine learning in HAR using three Feature Subsets: Feature Subset Signal Vector Magnitude (SMA), Feature Subset Fast Fourier Transform (FFT), and Feature Subset Value-Crossing. In features selection, ANOVA was used to reduce feature dimensionality. The experimental results have been assessed using the confusion matrix to prove that the proposed model can achieve an optimal accuracy of 0.97, higher than several state-of-the-art approaches. The optimal sensitivity and specificity values have been 0.98 and 0.99 and are partially higher than previous studies using similar testing scenarios.

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Human Motion Gesture Recognition Based on Computer Vision

Cited by 16 publications

References 30 publications

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning

A New Approach for Human Activity Recognition (HAR) Using A Single Tri-axial Accelerometer Based on a Combination of Three Feature Subsets

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