2020
DOI: 10.1109/access.2020.3020141
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Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images

Abstract: The main work of human motion gesture recognition is to recognize and analyze the behavior of human objects in the video. Although the current research in the field of human motion gesture recognition has achieved certain results, the human motion gesture recognition in real life scenes has great effects due to factors such as camera movement, target scale transformation, dynamic background, viewing angle, and illumination. This article first proposes a new method of constructing human motion posture features … Show more

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Cited by 28 publications
(9 citation statements)
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“…e gesture sequences of the athlete's body captured by the hardware are divided by the spatiotemporal graph convolution method shown above, and the joint points and link relationships are divided according to label subsets. For the matching algorithm of joints, the data structure can be used as the basis for modeling the athlete's joint points and limbs, extending their temporal dimension and modeling the action consisting of multiple poses using the spatiotemporal data structure [25,26]. For a given spatiotemporal graph convolution, the mapping of labels can be implemented according to uniform division, distance division, and division in space.…”
Section: Optimized Tracking Recognition Algorithmmentioning
confidence: 99%
“…e gesture sequences of the athlete's body captured by the hardware are divided by the spatiotemporal graph convolution method shown above, and the joint points and link relationships are divided according to label subsets. For the matching algorithm of joints, the data structure can be used as the basis for modeling the athlete's joint points and limbs, extending their temporal dimension and modeling the action consisting of multiple poses using the spatiotemporal data structure [25,26]. For a given spatiotemporal graph convolution, the mapping of labels can be implemented according to uniform division, distance division, and division in space.…”
Section: Optimized Tracking Recognition Algorithmmentioning
confidence: 99%
“…Compared with a traditional neural network, this algorithm adopts a convolution computation. The neurons between the convolutional layers of CNN are only connected with a few neurons between the adjacent layers, and the pooling and convolutional layers can respond to the translation invariance of the input features, effectively identifying the similar features of images (Bu, 2020;Gao et al, 2020). Moreover, CNN is composed of a convolutional layer for convolution operation, a pooling layer for feature screening, and a fully connected layer for feature fusion (Chen, 2019).…”
Section: Har Algorithm Based On Cnnmentioning
confidence: 99%
“…Still there are two challenges in recognizing of humans. First is to identify the appearance of distorted [7] humans. Next to recognize the occluded human's structure.…”
Section: Multi-person Action Recognitionmentioning
confidence: 99%