Research on Human Action Recognition Based on Convolutional Neural Network

Wang, Peng; Yang, Yuwei; Li, Wanchong; Zhang, Linhao; Wang, Mengyuan; Zhang, Xiaobo; Zhu, Man

doi:10.1109/wocc.2019.8770575

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…It can extract image features and is commonly used in areas related to image recognition. (2) ResNet3D [24,25]. 3D convolution kernel was used to extract the spatial-temporal characteristics, and the network structure was deeper (18 layers and 34 layers) than the C3D model.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

Zhang

Qing-dao-er-ji

2022

Mathematical Problems in Engineering

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Animated Graphics Interchange Format (GIF) images have become an important part of network information interaction, and are one of the main characteristics of analyzing social media emotions. At present, most of the research on GIF affection recognition fails to make full use of spatial-temporal characteristics of GIF images, which limits the performance of model recognition to a certain extent. A GIF emotion recognition algorithm based on ResNet-ConvGRU is proposed in this paper. First, GIF data is preprocessed, converting its image sequences to static image format for saving. Then, the spatial features of images and the temporal features of static image sequences are extracted with ResNet and ConvGRU networks, respectively. At last, the animated GIFs data features are synthesized and the seven emotional intensities of GIF data are calculated. The GIFGIF dataset is used to verify the experiment. From the experimental results, the proposed animated GIFs emotion recognition model based on ResNet-ConvGRU, compared with the classical emotion recognition algorithms such as VGGNet-ConvGRU, ResNet3D, CNN-LSTM, and C3D, has a stronger feature extraction ability, and sentiment classification performance. This method provides a finer-grained analysis for the study of public opinion trends and a new idea for affection recognition of GIF data in social media.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

Zhang

Qing-dao-er-ji

2022

Mathematical Problems in Engineering

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“…In the recognition phase, the sample to be tested is first transformed into predefined symbols by a basic recognition method, and then the words are arranged into an ordered stream of symbols according to a grammatical analysis technique (the syntax given). An event recognition language is defined to define the basic words that make up all actions in the world, and a context-independent grammar or stochastic context-independent grammar is used to model more advanced behaviors [14]. The syntax-based approach is good for recognizing temporal and more complex high-level behaviors, but it is difficult to handle concurrent events effectively.…”

Section: Syntax-based Recognition Methodsmentioning

confidence: 99%

“…The United States has a long history of physical fitness testing research that is relatively welldeveloped, with a high level of research, advanced academic thought, and testing techniques. Physical fitness research in the United States has been conducted more extensively and frequently, with a strong sense of purpose and planning for data collection, and a national youth physical fitness census is essentially conducted once every decade, with the emphasis still on the essential physical fitness testing work in school sports [8]. In terms of physical fitness monitoring, California, the third largest and most populous state in the United States, is the most representative of the states.…”

Section: Related Work 21 Physical Fitness Test Work Of College Studentsmentioning

confidence: 99%

A machine vision-based method for recognizing standard movements in college sports tests

2024

Preprint

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Currently, the physical health of college students is extremely concerning, as a significant number of them lack physical activity, resulting in a severe decline in physical health. To promote the physical health of students and ensure the health of all people, the state has issued a policy to include the physical health of college students in the assessment and evaluation of colleges and universities, and the physical health test of college students has become an important criterion in the assessment and evaluation of different colleges and universities. However, because many test movements must be evaluated and scored by professional coaches, the formalization and marginalization of sports tests in colleges and universities has resulted from the lack of professional sports test coaches and teachers. To increase the professionalism of sports test instructors and coaches, as well as to reduce the labor costs associated with sports tests. In this paper, we propose using machine vision to recognize the sports test actions of college students by extracting the depth information of the actions via vision sensors, combining the information of human skeletal nodes, and separating the action image information from the background by choosing an appropriate depth distance threshold. Using a support vector machine (SVM) algorithm, the segmented gesture images are recognized and their normality is assessed. The conclusion of the dynamic action recognition and evaluation process is reached. Extensive testing demonstrates that this method is able to recognize test actions in real time and provide accurate scores.

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A Lightweight Two-stream Fusion Deep Neural Network Based on ResNet Model for Sports Motion Image Recognition

Niu

2021

Sens Imaging

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Research on Human Action Recognition Based on Convolutional Neural Network

Cited by 5 publications

References 11 publications

Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

A machine vision-based method for recognizing standard movements in college sports tests

A Lightweight Two-stream Fusion Deep Neural Network Based on ResNet Model for Sports Motion Image Recognition

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