The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Laboratory Abnormal Behavior Detection Based on Multimodal Information Fusion
Abstract
The traditional laboratory anomaly detection methods mainly focus on the hidden dangers caused by chemical leaks and other items, ignoring the impact of abnormal behaviors such as incorrect operations and improper behavior on safety in the laboratory. This paper proposes a laboratory abnormal behavior detection method based on multimodal information fusion. The method generates a dense optical flow field of RGB image sequences based on optical flow theory and global smoothing constraints, and mines motion mode information. Meanwhile, the contour modal information of behavior is captured through convolution and adjacency matrix operations. Using decision level and proximity functions to integrate student behavior motion mode information and contour mode information, and using the maximum value as the behavior detection result. The experimental results show that the method can effectively detect abnormal behavior in the laboratory environment, with small detection errors and a specificity close to 1.00, effectively ensuring the safety of the laboratory environment.
Related Content
|
Shakir A. Mehdiyev, Tahmasib Kh. Fataliyev.
© 2024.
17 pages.
|
|
Fuhai Jia, Yanru Jia, Jing Li, Zhenghui Liu.
© 2024.
13 pages.
|
|
Dawei Zhang.
© 2024.
16 pages.
|
|
Xiao Han, Huiqiang Wang, Guoliang Yang, Chengbo Wang.
© 2024.
23 pages.
|
|
Areej Muqbil Alotibi, Salem Yahya Altaleedi, Tanveer Zia, Emad Ul Haq Qazi.
© 2024.
22 pages.
|
|
Dawei Zhang.
© 2024.
16 pages.
|
|
Lora Pitman.
© 2024.
14 pages.
|
|
|