Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching
In order to improve the teaching quality, this paper proposes a multi-modal feature fusion-based abnormal behavior detection method, aiming at the problems of false detection, missing detection and imbalance of positive and negative samples in the abnormal behavior detection of students in class. Th...
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-02-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202509-28-09-0002 |
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| author | Lei Kan Man Wang |
| author_facet | Lei Kan Man Wang |
| author_sort | Lei Kan |
| collection | DOAJ |
| description | In order to improve the teaching quality, this paper proposes a multi-modal feature fusion-based abnormal behavior detection method, aiming at the problems of false detection, missing detection and imbalance of positive and negative samples in the abnormal behavior detection of students in class. The new method consists of encoder module, detection module and decoder module. The encoder module is used to extract the characteristic information of students behavior image and transfer it to the detection module. The behavior detection module obtains more image information through the feature fusion group to reduce the color distortion and artifacts of the behavior image, and transfers the obtained image information to the deep normalization
correction convolution block to reduce the covariate shift and make the model easier to train. The multi-path feature convolution block can obtain image information with richer texture details. Finally, the decoder module converts the low-dimensional feature mapping back to the high-dimensional original input space through deconvolution and up-sampling operations to obtain the behavior detection image. |
| format | Article |
| id | doaj-art-ce4217a4333c4037964b7f37355e516d |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-ce4217a4333c4037964b7f37355e516d2025-01-31T16:05:51ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-02-012891657166610.6180/jase.202509_28(9).0002Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English TeachingLei Kan0Man Wang1Zhengzhou Vocational College of Intelligent Technology, 451161 Zhengzhou, ChinaGraduate school of Party School of the Central Committee of CP.C (National Academy of Governance), 100089 Beijing, China. College of Marxism, Shenzhen MSU-BIT University, 518000 Shenzhen ChinaIn order to improve the teaching quality, this paper proposes a multi-modal feature fusion-based abnormal behavior detection method, aiming at the problems of false detection, missing detection and imbalance of positive and negative samples in the abnormal behavior detection of students in class. The new method consists of encoder module, detection module and decoder module. The encoder module is used to extract the characteristic information of students behavior image and transfer it to the detection module. The behavior detection module obtains more image information through the feature fusion group to reduce the color distortion and artifacts of the behavior image, and transfers the obtained image information to the deep normalization correction convolution block to reduce the covariate shift and make the model easier to train. The multi-path feature convolution block can obtain image information with richer texture details. Finally, the decoder module converts the low-dimensional feature mapping back to the high-dimensional original input space through deconvolution and up-sampling operations to obtain the behavior detection image.http://jase.tku.edu.tw/articles/jase-202509-28-09-0002abnormal behavior detectionmultimodal feature fusionencoder-decodermulti-path feature convolution block |
| spellingShingle | Lei Kan Man Wang Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching Journal of Applied Science and Engineering abnormal behavior detection multimodal feature fusion encoder-decoder multi-path feature convolution block |
| title | Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching |
| title_full | Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching |
| title_fullStr | Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching |
| title_full_unstemmed | Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching |
| title_short | Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching |
| title_sort | behavior anomaly detection based on multi modal feature fusion and its application in english teaching |
| topic | abnormal behavior detection multimodal feature fusion encoder-decoder multi-path feature convolution block |
| url | http://jase.tku.edu.tw/articles/jase-202509-28-09-0002 |
| work_keys_str_mv | AT leikan behavioranomalydetectionbasedonmultimodalfeaturefusionanditsapplicationinenglishteaching AT manwang behavioranomalydetectionbasedonmultimodalfeaturefusionanditsapplicationinenglishteaching |