Laboratory Abnormal Behavior Detection Based on Multimodal Information Fusion

被引:0
|
作者
Zhang, Dawei [1 ]
机构
[1] Liaodong Univ, Sch Informat Engn, Liaodong, Peoples R China
关键词
Multimodal Information Fusion; Optical Flow Theory; Abnormal Behavior Detection; Motion Mode Information; Contour Modal Information;
D O I
10.4018/IJDCF.350265
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页数:16
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