Human Action Recognition Using CNN-SVM Model

Athavale, Vijay Anant; Gupta, S. C.; Kumar, Deepak; Savita, .

doi:10.4028/www.scientific.net/ast.105.282

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…The real development of image recognition processing technology was in the 1990s, and the real leap forward is in the 21st century. At this time, image recognition technology has been widely used and valued in many industries and fields, including medical industry, military industry, and agriculture [1,2].…”

Section: Introductionmentioning

confidence: 99%

Recognition of English Vocabulary and Speech Corpus Based on Computer Image Processing

Sun¹

2022

Wireless Communications and Mobile Computing

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In order to solve the problem that the function and performance of traditional systems in English machine translation cannot meet the needs of intelligence, the author proposes an English vocabulary and speech corpus recognition system based on computer image processing. On the basis of designing the overall structure of the system, the hardware structure is designed by designing the server and translator. In the software design, the semantic features of the input English sentences in human-computer interaction are analyzed by using the enhanced algorithm, the decoding algorithm is designed according to the analysis results, and the English machine translation model is constructed. Experimental results show that when the number of sentences translated by this system is from 100 to 1000, the BLEU indicator keeps rising from 7 to 10. The English vocabulary and speech corpus recognition system based on computer image processing is more efficient.

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Section: Introductionmentioning

confidence: 99%

Recognition of English Vocabulary and Speech Corpus Based on Computer Image Processing

Sun¹

2022

Wireless Communications and Mobile Computing

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“…In Table 5, Athavale et al 20 . proposed a method for human action recognition using the CNN-support vector machine (SVM) model, which combines CNN and SVM.…”

Section: Methodsmentioning

confidence: 99%

“…In Table 5, Athavale et al 20 proposed a method for human action recognition using the CNN-support vector machine (SVM) model, which combines CNN and SVM. The advantage of this method is that SVM is used to replace the FC layer of VGG16Net so as to reduce the parameters and improve the operation speed.…”

Section: Model Validity Verificationmentioning

confidence: 99%

Posture recognition method of duty personnel based on human posture key points and convolutional neural network

Deng,

Sheng,

Pei

et al. 2024

J. Electron. Imag.

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To guarantee the safety and efficiency of industrial production and prevent accidents or losses caused by personnel negligence or negligence, this work proposes a personnel on-duty status recognition method. The method combines a human pose estimation algorithm and a target detection algorithm, which can automatically discriminate six states of personnel on duty. First, the original image is processed using a high-resolution network (HRNet) to generate human pose keypoint maps. Then SE-VGG16 is constructed by combining the squeeze-excitation network and VGG16 for feature extraction of human pose keypoint maps. Finally, the design of the lightweight convolutional neural network for primary classification and you only look once version 5 is used for reclassification for behaviors with similar action features. The experimental results show that the method has an average recognition accuracy of 98.27% with good robustness and generalization ability for six kinds of personnel on-duty status in multiple environments.

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“…For example, the kinetic energy of the joints of human body changes greatly and changes quickly when people kick, while the kinetic energy of the joints of human body changes relatively gently, and the change frequency is low when people jog. Therefore, it can be seen intuitively that the kinetic energy of joints is an important feature of human behavior recognition [ 10 – 12 ]. According to the length proportion of each part of the anatomy of the human body and the result of locating the end of the trunk, the joint proportion is used to locate the inflection point of the limbs.…”

Section: Recognition Methods Of Wushu Human Complex Movement Based On...mentioning

confidence: 99%

Recognition Method of Wushu Human Complex Movement Based on Bone Point Feature

Zhang

Tao

2022

Computational and Mathematical Methods in Medicine

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The existing recognition methods of complex human movements in Wushu have the problem of imperfect kinetic energy model, which leads to low recognition accuracy. A complex human motion recognition method based on bone point features is designed. Identify martial arts movement posture, combine the upward movement of human center of gravity trajectory, establish the kinetic energy model of joints according to the positioning results of extremity points, set the threshold of local spatial differences of human bones with the central node of hip joint as the center point, avoid overcalculation, and optimize the complex motion identification process by combining the characteristics of bone points. Experimental Results. The correct rate of different types of actions identified by this method is 90.1% and 92.7%, and the identification time is 1.2 s and 1.41 s, which shows that this method can identify actions quickly and effectively by combining the feature information of bone points.

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Human Action Recognition Using CNN-SVM Model

Cited by 19 publications

References 15 publications

Recognition of English Vocabulary and Speech Corpus Based on Computer Image Processing

Recognition of English Vocabulary and Speech Corpus Based on Computer Image Processing

Posture recognition method of duty personnel based on human posture key points and convolutional neural network

Recognition Method of Wushu Human Complex Movement Based on Bone Point Feature

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