Research on Neural Network Technology in Public Safety Anomaly and Hazard Detection
DOI:
https://doi.org/10.54097/xqgc7a16Keywords:
Public Safety, Abnormal Hazard Detection, Neural Network, DoS Attack, Synchronous Control, Multi-source FusionAbstract
Abnormal hazards in public places have the characteristics of low frequency, diversity and strong timeliness. Single video model is easily affected by occlusion, illumination and network interruption. In this article, an abnormal danger detection method is constructed, which combines the temporal and spatial characteristics of video, environmental sensing information and communication status, and introduces the neural network synchronization control mechanism under DoS attack. The model uses convolutional network to extract local appearance and motion information, describes the evolution of events with gated time series units, and then completes multi-source feature fusion through attention mechanism. When DoS attacks cause intermittent loss of node data, state observation and synchronization compensation are introduced to maintain the time consistency between the edge detection results and the central model. The experimental results show that the F1 value of the proposed method reaches 0.922, which remains at 0.861 under the packet loss rate of 40%. Synchronous compensation reduces the average state error by about 41.7%. The research shows that the combination of dangerous content identification and network operation state can improve the stability of public safety anomaly detection under the condition of disturbed communication.
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[1] Pang, G., Shen, C., Cao, L., & van den Hengel, A. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), 1 38. https://doi.org/10.1145/3439950.
[2] Nayak, R., Pati, U. C., & Das, S. K. (2021). A comprehensive review on deep learning based methods for video anomaly detection. Image and Vision Computing, 106, 104078.
[3] Gandapur, M. Q. (2022). E2E VSDL: End to end video surveillance based deep learning model to detect and prevent criminal activities. Image and Vision Computing, 123, 104467. https://doi.org/10.1016/j.imavis.2022.104467.
[4] Ingle, P. Y., & Kim, Y. G. (2022). Real time abnormal object detection for video surveillance in smart cities. Sensors, 22(10), 3862. https://doi.org/10.3390/s22103862.
[5] Bajgoti, A., Gupta, R., Balaji, P., & Dwivedi, R. (2023). SwinAnomaly: Real time video anomaly detection using Video Swin Transformer and SORT. IEEE Access, 11, 111094 111105. https://doi.org/10.1109/ ACCESS. 2023. 332 1801.
[6] Huszár, V. D., Adhikarla, V. K., Négyesi, I., & Krasznay, C. (2023). Toward fast and accurate violence detection for automated video surveillance applications. IEEE Access, 11, 18772 18793. https://doi.org/10.1109/ACCESS.2023.3245521.
[7] Kotkar, V. A., & Sucharita, V. (2023). Fast anomaly detection in video surveillance system using robust spatiotemporal and deep learning methods. Multimedia Tools and Applications, 82, 34259 34286. https://doi.org/10.1007/s11042 023 14840 0.
[8] Huang, X., Ding, D. W., Li, Z., & An, C. (2022). Distributed security control for complex cyber physical systems against denial of service attacks. International Journal of Control, Automation and Systems, 20(2), 421 431. https://doi.org/ 10. 1007/ s12555 020 0906 7.
[9] Liu, J., Shen, M., & Xie, X. (2023). Secure consensus control for multi agent systems subject to consecutive asynchronous denial of service attacks. International Journal of Control, Automation and Systems, 21, 2214 2226. https://doi.org/ 10. 1007/s12555 021 0564 4.
[10] Wen, G., Wang, P., Lv, Y., Chen, G., & Zhou, J. (2023). Secure consensus of multi agent systems under denial of service attacks. Asian Journal of Control, 25(2), 695 709. https://doi. org/ 10. 1002/asjc.2953.
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