Analysis of Art Classroom Teaching Behavior Based on Intelligent Image Recognition
To solve the problem of intelligent image recognition in classroom behavior, this paper proposes a fast target detection based on FFmpeg CODEC, extracts MHI-HOG joint features according to the located foreground target area, and finally completes the behavior recognition model through a BP neural ne...
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| Veröffentlicht in: | Mobile information systems 2022-08, Vol.2022, p.1-11 |
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| container_title | Mobile information systems |
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| creator | Gu, Chihui Li, Yinxing |
| description | To solve the problem of intelligent image recognition in classroom behavior, this paper proposes a fast target detection based on FFmpeg CODEC, extracts MHI-HOG joint features according to the located foreground target area, and finally completes the behavior recognition model through a BP neural network support vector machine joint classifier based on the look-up table. The results are as follows: the motion detection method based on H.264 FFmpeg CODEC video has the highest detection accuracy, which can reach 95%. The foreground detection method takes about 10 ms and saves 90% of the time. The behavior classification and recognition effect of MHI-HOG joint features based on the model has been significantly improved, and the comprehensive recognition rate has reached 95%. The built-in BP neural network support vector machine has 97% accuracy in extracting, classifying, and recognizing the characteristics of a single sample. This study attempts to identify and analyze the class behavior and validate the effectiveness of the collaborative classifiers proposed in this paper to build an intellectual classroom. |
| doi_str_mv | 10.1155/2022/5736407 |
| format | Article |
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The results are as follows: the motion detection method based on H.264 FFmpeg CODEC video has the highest detection accuracy, which can reach 95%. The foreground detection method takes about 10 ms and saves 90% of the time. The behavior classification and recognition effect of MHI-HOG joint features based on the model has been significantly improved, and the comprehensive recognition rate has reached 95%. The built-in BP neural network support vector machine has 97% accuracy in extracting, classifying, and recognizing the characteristics of a single sample. This study attempts to identify and analyze the class behavior and validate the effectiveness of the collaborative classifiers proposed in this paper to build an intellectual classroom.</description><identifier>ISSN: 1574-017X</identifier><identifier>EISSN: 1875-905X</identifier><identifier>DOI: 10.1155/2022/5736407</identifier><language>eng</language><publisher>Amsterdam: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Back propagation networks ; Cameras ; Classification ; Classifiers ; Classrooms ; Codec ; Educational technology ; Feature extraction ; Learning ; Lookup tables ; Motion perception ; Object recognition ; R&D ; Research & development ; Research methodology ; Student behavior ; Support vector machines ; Surveillance ; Target detection ; Teachers ; Teaching</subject><ispartof>Mobile information systems, 2022-08, Vol.2022, p.1-11</ispartof><rights>Copyright © 2022 Chihui Gu and Yinxing Li.</rights><rights>Copyright © 2022 Chihui Gu and Yinxing Li. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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The results are as follows: the motion detection method based on H.264 FFmpeg CODEC video has the highest detection accuracy, which can reach 95%. The foreground detection method takes about 10 ms and saves 90% of the time. The behavior classification and recognition effect of MHI-HOG joint features based on the model has been significantly improved, and the comprehensive recognition rate has reached 95%. The built-in BP neural network support vector machine has 97% accuracy in extracting, classifying, and recognizing the characteristics of a single sample. 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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Algorithms Artificial intelligence Back propagation networks Cameras Classification Classifiers Classrooms Codec Educational technology Feature extraction Learning Lookup tables Motion perception Object recognition R&D Research & development Research methodology Student behavior Support vector machines Surveillance Target detection Teachers Teaching |
| title | Analysis of Art Classroom Teaching Behavior Based on Intelligent Image Recognition |
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