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
相关论文
共 50 条
  • [1] The Recognition of Teacher Behavior Based on Multimodal Information Fusion
    Wu, Dongli
    Chen, Jia
    Deng, Wei
    Wei, Yantao
    Luo, Heng
    Wei, Yangyu
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020 (2020)
  • [2] Multitarget Detection Algorithm Based on Multimodal Information Fusion
    Liu Tong
    Gao Sijie
    Nie Weizhi
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2022, 59 (08)
  • [3] Research on Pedestrian Detection Based on Multimodal Information Fusion
    Yang, Xiaoping
    Li, Zhehong
    Liu, Yuan
    Huang, Ran
    Tan, Kai
    Huang, Lin
    [J]. INFORMATION TECHNOLOGY AND CONTROL, 2023, 52 (04): : 1045 - 1057
  • [4] Multimodal-based abnormal behavior detection method in virtualization environment
    Zheng, Luxin
    Zhang, Jian
    Wang, Xiangyi
    Lin, Faxin
    Meng, Zheng
    [J]. COMPUTERS & SECURITY, 2024, 143
  • [5] Multimodal information fusion for video concept detection
    Wu, Y
    Lin, CK
    Chang, EY
    Smith, JR
    [J]. ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5, 2004, : 2391 - 2394
  • [6] Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network
    Li, Ming
    Kuang, Lishan
    Xu, Shuhua
    Sha, Zhanguo
    [J]. IEEE ACCESS, 2019, 7 : 180134 - 180146
  • [7] Fatigue driving detection of urban road at night based on multimodal information fusion
    Wang, W.X.
    Sun, B.G.
    Xia, R.
    [J]. Advances in Transportation Studies, 2023, 2 (Special issue): : 171 - 188
  • [8] Multimodal Information Fusion for Robust Heart Beat Detection
    Ding, Quan
    Bai, Yong
    Erol, Yusuf Bugra
    Salas-Boni, Rebeca
    Zhang, Xiaorong
    Li, Lei
    Hu, Xiao
    [J]. 2014 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), VOL 41, 2014, 41 : 261 - 264
  • [9] Video Abnormal Behavior Detection Based on Human Skeletal Information and GRU
    Li, Yibo
    Zhang, Zixun
    [J]. INTELLIGENT ROBOTICS AND APPLICATIONS (ICIRA 2022), PT II, 2022, 13456 : 450 - 458
  • [10] Semantic based information fusion in a multimodal interface
    Russ, G
    Sallans, B
    Hareter, H
    [J]. HCI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION, 2005, : 94 - 100