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

Author

Listed:
  • Guoqiang Sun

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Peng Xu

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Man Guo

    (Ministry of Science and Technology, Gwacheon 13809, Korea)

  • Hao Sun

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Zhaochen Du

    (Hisense State Key Laboratory of Digital Multimedia Technology, Qingdao 266061, China)

  • Yujun Li

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Bin Zhou

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

Abstract

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.

Suggested Citation

  • Guoqiang Sun & Peng Xu & Man Guo & Hao Sun & Zhaochen Du & Yujun Li & Bin Zhou, 2022. "OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log," Mathematics, MDPI, vol. 10(13), pages 1-14, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2292-:d:852840
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    References listed on IDEAS

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    1. Shaonian Huang & Dongjun Huang & Xinmin Zhou, 2018. "Learning Multimodal Deep Representations for Crowd Anomaly Event Detection," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, January.
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