Intelligent Frame Retention and Anomaly Detection with Notification Using YOLOv11m
DOI:
https://doi.org/10.13052/jmm1550-4646.2221Keywords:
Crime detection, deep learning, video storage optimization, real-time anomaly detection, security monitoringAbstract
This study aims to solve the problem of video storage and improves the overall efficiency of cameras by adopting real-time anomaly detection, hence informing the user about any suspicious anomalies. The proposed trained model processes live video streams, identifying unusual events and anomalies such as theft, weapons, or violent activities. Simultaneously, a video storage optimization algorithm reduces redundant frames while maintaining movement detected video streams from CCTV surveillance. In addition, if the model detects an unusual event occurring in the live video stream, it immediately notifies the user about the type of anomaly and the location of the event that occurred. Experimental results demonstrate that the proposed system effectively detects anomalies with an average precision–recall score of 0.958 and an F1 confidence score of 0.93, ensuring reliable threat identification and detection. The model is robust and differentiates between normal and anomalous activities as justified by experimental results.
Downloads
References
M. M. Ali, “Real-time video anomaly detection for smart surveillance,” IET Image Process., Dec. 2022, doi: 10.1049/ipr2.12720.
V. Singh, S. Singh, and P. Gupta, “Real-time anomaly recognition through CCTV using neural networks,” Procedia Comput. Sci., vol. 173, pp. 254–263, 2020, doi: 10.1016/j.procs.2020.06.030.
Fellows Research, “CCTV Surveillance Trends,” 2021.
Western Digital, “CCTV storage: Safely managing and deleting surveillance footage,” Western Digital Blog. [Online]. Available: https://www.westerndigital.com/en-in/solutions/cctv/blog/cctv-storage-managing-deletingsurveillance-footage. Accessed: Jan. 7, 2025.
S. Leroux, B. Li, and P. Simoens, “Multi-branch neural networks for video anomaly detection in adverse lighting and weather conditions,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis. (WACV), Waikoloa, HI, USA, 2022, pp. 3027–3035, doi: 10.1109/WACV51458.2022.003086.
M. F. B. A. Rahman, Smart CCTVs for Secure Cities: Potentials and Challenges,Policy Report. S. Rajaratnam School of International Studies, Nanyang Technological University, July 2017.
S. Arora, K. Bhatia, and V. Amit, “Storage optimization of video surveillance from CCTV camera,” in Proc. 2nd Int. Conf. Next Gen. Comput. Technol. (NGCT), 2016, doi: 10.1109/NGCT.2016.7877503.
Roboflow, “Anomaly Detection Dataset,” [Online]. Available: https://universe.roboflow.com/smartsurveillance/anomaly-2k9fc. Accessed: Feb. 7, 2025.
Google, “Google Colaboratory,” [Online]. Available: https://colab.research.google.com/. Accessed: Feb. 7, 2025.
H. Afreen, M. Kashif, Q. Shaheen, Y. H. Alfaifi, and M. Ayaz, “IoT-Based Smart Surveillance System for High-Security Areas,” Appl. Sci., vol. 13, no. 15, p. 8936, Aug. 2023, doi: 10.3390/app13158936.
S. Ahmed, M. T. Bhatti, M. G. Khan, B. L¨ovstr¨om, and M. Shahid, “Development and optimization of deep learning models for weapon detection in surveillance videos,” Appl. Sci., vol. 12, no. 12, p. 5772, June 2022, doi: 10.3390/app12125772.
P. Y. Ingle and Y.-G. Kim, “Real-time abnormal object detection for video surveillance in smart cities,” Sensors, vol. 22, no. 10, p. 3862, May 2022, doi: 10.3390/s22103862.
H. Jeon, H. Kim, D. Kim, and J. Kim, “PASS-CCTV: Proactive Anomaly Surveillance System for CCTV Footage Analysis in Adverse Environmental Conditions,” Expert Syst. Appl., vol. 254, p. 124391, Nov. 2024, doi: 10.1016/j.eswa.2024.124391.
M. Y. M. Manu, R. G. K. Ravikumar, and S. S. V. Shashikala, “Anomaly alert system using CCTV surveillance,” in Proc. IEEE 2nd Mysore Sub Sect. Int. Conf. (MysuruCon), Oct. 2022, doi: 10.1109/MysuruCon55714.2022.9972363.
V. Shukla, G. K. Singh, and P. Shah, “Automatic alert of security threat through video surveillance system,” in Proc. 54th Inst. Nucl. Mater. Manage. Annu. Meeting, Palm Desert, CA, USA, Jul. 2013.
J. Tatiya, R. Makhija, M. Pathe, and S. Late, “Anomaly detection for video surveillance,”Int. J. Sci. Res. Sci. Technol., vol. 7, no. 3, pp. 1–5, May 2021, doi: 10.32628/IJSRSR21869.
University of Central Florida, “Real-world dataset project,” 2024. [Online]. Available: https://www.crcv.ucf.edu/projects/real-world/. Accessed: Oct. 20, 2024.
Y.K. Wang, C.T. Fan, C. Y. Ke, and P. S. Deng, “Real-time camera anomaly detection for real world video surveillance,” in Proc. Int. Conf. Mach. Learn. Cybern. (ICMLC), vol. 4, Aug. 2011, doi: 10.1109/ICMLC.2011.6017032.
J. T. Zhou, J. Du, H. Zhu, and X. Peng, “AnomalyNet: An anomaly detection network for video surveillance,” IEEE Trans. Inf. Forensics Security, vol. PP, no. 99, pp. 1–1, Feb. 2019, doi: 10.1109/TIFS.2019.2900907.
W. Sultani, C. Chen, and M. Shah, “Real-world anomaly detection in surveillance videos,” arXiv preprint arXiv:1801.04264, Jan. 2018, doi: 10.48550/arXiv.1801.04264.
https://universe.roboflow.com/smartsurveillance/anomaly-2k9fc. [Accessed: Apr. 4, 2024]. https://universe.roboflow.com/smartsurveillance/anomaly-2k9fc. Accessed: Mar. 10, 2025.
