Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry
Strengthening the safety management in confined space operations is an important foundation for preventing and reducing production safety accidents. In the grain soil, which are large enclosed spaces insufficient lighting and restricted air circulation create significant safety risks during the oper...
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| Format: | Article |
| Language: | English |
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Academy of National Food and Strategic Reserves Administration
2025-05-01
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| Series: | Liang you shipin ke-ji |
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| Online Access: | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250322 |
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| author | CHEN Wei-dong DING Jun-dan HAN Zhi-qiang HE Wei ZHANG Feng |
| author_facet | CHEN Wei-dong DING Jun-dan HAN Zhi-qiang HE Wei ZHANG Feng |
| author_sort | CHEN Wei-dong |
| collection | DOAJ |
| description | Strengthening the safety management in confined space operations is an important foundation for preventing and reducing production safety accidents. In the grain soil, which are large enclosed spaces insufficient lighting and restricted air circulation create significant safety risks during the operation. Utilizing in-silo surveillance video to detect and analyze the behavior of workers is a crucial technical measure to ensure safe operations. This paper summarizes the methods for establishing and preprocessing datasets for video-based abnormal behavior detection in grain storage operations, explains the progress of machine learning and deep learning technologies in this field, including technological innovation and practical applications in areas such as abnormal behavior detection and real-time early warning. In addition, the paper reviews the research findings and existing problems in this field, such as incomplete datasets, insufficient model accuracy, etc., and provides an outlook on future research directions. |
| format | Article |
| id | doaj-art-43024d0abc9f4f81abfaf2d2c42bc59d |
| institution | Kabale University |
| issn | 1007-7561 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Academy of National Food and Strategic Reserves Administration |
| record_format | Article |
| series | Liang you shipin ke-ji |
| spelling | doaj-art-43024d0abc9f4f81abfaf2d2c42bc59d2025-08-20T03:44:46ZengAcademy of National Food and Strategic Reserves AdministrationLiang you shipin ke-ji1007-75612025-05-0133320421010.16210/j.cnki.1007-7561.2025.03.021Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage IndustryCHEN Wei-dong0DING Jun-dan1 HAN Zhi-qiang2HE Wei3ZHANG Feng4 Henan University of Technology, College of Information Science and Engineering, Zhengzhou, Henan 450001, China;Henan University of Technology, National Engineering Research Center for Grain Storage and Transportation, Zhengzhou, Henan 450001, China Henan University of Technology, College of Information Science and Engineering, Zhengzhou, Henan 450001, ChinaZhongshan Grain Reserve Management Co., Zhongshan, Guangdong 528400, ChinaHenan University of Technology, College of Information Science and Engineering, Zhengzhou, Henan 450001, ChinaZhongshan Grain Reserve Management Co., Zhongshan, Guangdong 528400, ChinaStrengthening the safety management in confined space operations is an important foundation for preventing and reducing production safety accidents. In the grain soil, which are large enclosed spaces insufficient lighting and restricted air circulation create significant safety risks during the operation. Utilizing in-silo surveillance video to detect and analyze the behavior of workers is a crucial technical measure to ensure safe operations. This paper summarizes the methods for establishing and preprocessing datasets for video-based abnormal behavior detection in grain storage operations, explains the progress of machine learning and deep learning technologies in this field, including technological innovation and practical applications in areas such as abnormal behavior detection and real-time early warning. In addition, the paper reviews the research findings and existing problems in this field, such as incomplete datasets, insufficient model accuracy, etc., and provides an outlook on future research directions.http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250322grain storagevideoabnormal behavior detectiondeep learning |
| spellingShingle | CHEN Wei-dong DING Jun-dan HAN Zhi-qiang HE Wei ZHANG Feng Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry Liang you shipin ke-ji grain storage video abnormal behavior detection deep learning |
| title | Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry |
| title_full | Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry |
| title_fullStr | Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry |
| title_full_unstemmed | Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry |
| title_short | Research Progress on Video-based Abnormal Behavior Detection in the Grain Storage Industry |
| title_sort | research progress on video based abnormal behavior detection in the grain storage industry |
| topic | grain storage video abnormal behavior detection deep learning |
| url | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250322 |
| work_keys_str_mv | AT chenweidong researchprogressonvideobasedabnormalbehaviordetectioninthegrainstorageindustry AT dingjundan researchprogressonvideobasedabnormalbehaviordetectioninthegrainstorageindustry AT hanzhiqiang researchprogressonvideobasedabnormalbehaviordetectioninthegrainstorageindustry AT hewei researchprogressonvideobasedabnormalbehaviordetectioninthegrainstorageindustry AT zhangfeng researchprogressonvideobasedabnormalbehaviordetectioninthegrainstorageindustry |