OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

A surveillance system with more than hundreds of cameras and much fewer monitors strongly relies on manual scheduling and inspections from monitoring personnel. A monitoring method which improves the surveillance performance by analyzing and learning from a large amount of manual operation logs is p...

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Bibliographic Details
Published inMathematics (Basel) Vol. 10; no. 13; p. 2292
Main Authors Sun, Guoqiang, Xu, Peng, Guo, Man, Sun, Hao, Du, Zhaochen, Li, Yujun, Zhou, Bin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2022
Subjects
Algorithms
Behavior
Coders
Coding
Deep learning
Electronic surveillance
Image processing
joint coding model
Mathematics
Methods
Monitoring
monitoring strategy
monitoring system
Monitoring systems
Neural networks
operation log
Recall
recommendation
Support vector machines
Surveillance
Surveillance systems
System effectiveness
Technology application
China
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Summary:A surveillance system with more than hundreds of cameras and much fewer monitors strongly relies on manual scheduling and inspections from monitoring personnel. A monitoring method which improves the surveillance performance by analyzing and learning from a large amount of manual operation logs is proposed in this paper. Compared to fixed rules or existing computer-vision methods, the proposed method can more effectively learn from the operators’ behaviors and incorporate their intentions into the monitoring strategy. To the best of our knowledge, this method is the first to apply a monitoring-strategy recommendation model containing a global encoder and a local encoder in monitoring systems. The local encoder can adaptively select important items in the operating sequence to capture the main purpose of the operator, while the global encoder is used to summarize the behavior of the entire sequence. Two experiments are conducted on two data sets. Compared with att-RNN and att-GRU, the joint coding model in experiment 1 improves the Recall@20 by 9.4% and 4.6%, respectively, and improves the MRR@20 by 5.49% and 3.86%, respectively. In experiment 2, compared with att-RNN and att-GRU, the joint coding model improves by 11.8% and 6.2% on Recall@20, and improves by 7.02% and 5.16% on MRR@20, respectively. The results illustrate the effectiveness of the our model in monitoring systems.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10132292