A Transformer-Based Model for Abnormal Activity Recognition
Given the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal ac...
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| Format: | Article |
| Language: | fas |
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Semnan University
2024-04-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_8569_1733b9593b801778c3d2c53dbebcc0b4.pdf |
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| author | Amir Mohammad Ahmadi Kourosh Kiani Razieh Rastgoo |
| author_facet | Amir Mohammad Ahmadi Kourosh Kiani Razieh Rastgoo |
| author_sort | Amir Mohammad Ahmadi |
| collection | DOAJ |
| description | Given the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal activities. In this regard, various intelligent models have been proposed for detecting activities in videos. Considering recent advances in artificial intelligence, particularly deep learning, this paper introduces a model based on the Transformer network. To reduce computational complexity, keypoints of the human body are utilized in this approach. Fifteen key body points are input into the Transformer model, leveraging parallel processing during training and a self-attention mechanism. This enhances the speed and accuracy of the model. Experimental results on the JHMDB public database indicate an improvement in the accuracy of detecting abnormal activities compared to baseline models. |
| format | Article |
| id | doaj-art-b00efdefb9714646a0e5322f2a163c08 |
| institution | Kabale University |
| issn | 2008-4854 2783-2538 |
| language | fas |
| publishDate | 2024-04-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj-art-b00efdefb9714646a0e5322f2a163c082025-01-15T08:14:58ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382024-04-01227621322110.22075/jme.2024.32914.26048569A Transformer-Based Model for Abnormal Activity RecognitionAmir Mohammad Ahmadi0Kourosh Kiani1Razieh Rastgoo2Master's student, Faculty of Electrical and Computer Science, Semnan University, Semnan, IranAssociate Professor, Faculty of Electrical and Computer Science, Semnan University, Semnan, IranAssistant Professor, Electrical and Computer Faculty, Semnan University, Semnan, IranGiven the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal activities. In this regard, various intelligent models have been proposed for detecting activities in videos. Considering recent advances in artificial intelligence, particularly deep learning, this paper introduces a model based on the Transformer network. To reduce computational complexity, keypoints of the human body are utilized in this approach. Fifteen key body points are input into the Transformer model, leveraging parallel processing during training and a self-attention mechanism. This enhances the speed and accuracy of the model. Experimental results on the JHMDB public database indicate an improvement in the accuracy of detecting abnormal activities compared to baseline models.https://modelling.semnan.ac.ir/article_8569_1733b9593b801778c3d2c53dbebcc0b4.pdfvideo processingvideo surveillanceabnormal activitiesdeep learningtransformer network |
| spellingShingle | Amir Mohammad Ahmadi Kourosh Kiani Razieh Rastgoo A Transformer-Based Model for Abnormal Activity Recognition مجله مدل سازی در مهندسی video processing video surveillance abnormal activities deep learning transformer network |
| title | A Transformer-Based Model for Abnormal Activity Recognition |
| title_full | A Transformer-Based Model for Abnormal Activity Recognition |
| title_fullStr | A Transformer-Based Model for Abnormal Activity Recognition |
| title_full_unstemmed | A Transformer-Based Model for Abnormal Activity Recognition |
| title_short | A Transformer-Based Model for Abnormal Activity Recognition |
| title_sort | transformer based model for abnormal activity recognition |
| topic | video processing video surveillance abnormal activities deep learning transformer network |
| url | https://modelling.semnan.ac.ir/article_8569_1733b9593b801778c3d2c53dbebcc0b4.pdf |
| work_keys_str_mv | AT amirmohammadahmadi atransformerbasedmodelforabnormalactivityrecognition AT kouroshkiani atransformerbasedmodelforabnormalactivityrecognition AT raziehrastgoo atransformerbasedmodelforabnormalactivityrecognition AT amirmohammadahmadi transformerbasedmodelforabnormalactivityrecognition AT kouroshkiani transformerbasedmodelforabnormalactivityrecognition AT raziehrastgoo transformerbasedmodelforabnormalactivityrecognition |