1
Al-Turath University College, Baghdad, 10021, Iraq
(mohammed.maktof@turath.edu.iq)
2
Department of Computer Techniques Engineering, Al-Rafidain University College, Baghdad 10064, Iraq
(ibraheem.hatem.elc@ruc.edu.iq)
3
Department of Computer Techniques Engineering, Mazaya University College, Thi Qar, Iraq
(mohammed.wahab@mpu.edu.iq)
4
Department of Medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq
(ahmed.abbas@alfarahidiuc.edu.iq)
5
Department of Buildings and Construction Techniques Engineering, Al-Mustaqbal University College, 51001 Hillah, Babylon, Iraq
(alimajdi@uomus.edu.iq)
Abstract :
The proposed method of using Machine Learning in Motion Detection and Pedestrian Tracking-assisted Intelligent Video Surveillance Systems (ML-IVSS) can be seen as an application of intelligent fusion techniques. ML-IVSS combines the power of motion detection, pedestrian tracking, and machine learning to create a more accurate and efficient surveillance system for smart cities. By fusing these techniques, ML-IVSS can effectively detect unusual behaviors such as trespassing, interruption, crime, or fall-down, and provide accurate depth data from surveillance footage to protect residents. Intelligent fusion techniques can help improve the accuracy and effectiveness of surveillance systems in smart cities, making them safer and more secure for residents. Combination channel models are used at first, and an object area with prominent features is selected for surveillance. Scaled modification and extraction of features are carried out on the presumed object's region. Identifying the low-level characteristic is the first step in incorporating it into neural architectures for deep feature learning. A smart CCTV data set is used to evaluate the proposed method's performance. According to the numerical analysis, the proposed ML-IVSS model outperforms other traditional approaches in terms of abnormal behaviour detection (98.8%), prediction (97.4%), accuracy (96.9%), F1-score (97.1%), precision (95.6%), and recall (96.2%).
Keywords :
Video Surveillance System; Machine Learning; Smart City; Intelligent Fusion Techniques; Deep Feature Learning.
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| Style | # |
|---|---|
| MLA | Mohammed A. J. Maktoof, Ibraheem H. M., Mohammed A. Abdul Razzaq, Ahmed Abbas, Ali Majdi. "Machine Learning-Based Intelligent Video Surveillance in Smart City Framework." Fusion: Practice and Applications, Vol. 11, No. 2, 2023 ,PP. 35-47 (Doi : https://doi.org/10.54216/FPA.110203) |
| APA | Mohammed A. J. Maktoof, Ibraheem H. M., Mohammed A. Abdul Razzaq, Ahmed Abbas, Ali Majdi. (2023). Machine Learning-Based Intelligent Video Surveillance in Smart City Framework. Journal of Fusion: Practice and Applications, 11 ( 2 ), 35-47 (Doi : https://doi.org/10.54216/FPA.110203) |
| Chicago | Mohammed A. J. Maktoof, Ibraheem H. M., Mohammed A. Abdul Razzaq, Ahmed Abbas, Ali Majdi. "Machine Learning-Based Intelligent Video Surveillance in Smart City Framework." Journal of Fusion: Practice and Applications, 11 no. 2 (2023): 35-47 (Doi : https://doi.org/10.54216/FPA.110203) |
| Harvard | Mohammed A. J. Maktoof, Ibraheem H. M., Mohammed A. Abdul Razzaq, Ahmed Abbas, Ali Majdi. (2023). Machine Learning-Based Intelligent Video Surveillance in Smart City Framework. Journal of Fusion: Practice and Applications, 11 ( 2 ), 35-47 (Doi : https://doi.org/10.54216/FPA.110203) |
| Vancouver | Mohammed A. J. Maktoof, Ibraheem H. M., Mohammed A. Abdul Razzaq, Ahmed Abbas, Ali Majdi. Machine Learning-Based Intelligent Video Surveillance in Smart City Framework. Journal of Fusion: Practice and Applications, (2023); 11 ( 2 ): 35-47 (Doi : https://doi.org/10.54216/FPA.110203) |
| IEEE | Mohammed A. J. Maktoof, Ibraheem H. M., Mohammed A. Abdul Razzaq, Ahmed Abbas, Ali Majdi, Machine Learning-Based Intelligent Video Surveillance in Smart City Framework, Journal of Fusion: Practice and Applications, Vol. 11 , No. 2 , (2023) : 35-47 (Doi : https://doi.org/10.54216/FPA.110203) |