2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 11 , Issue 2 , PP: 35-47, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed A. J. Maktoof 1 * , Ibraheem H. M. 2 , Mohammed A. Abdul Razzaq 3 , Ahmed Abbas 4 , Ali Majdi 5
Doi: https://doi.org/10.54216/FPA.110203
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%).
Video Surveillance System , Machine Learning , Smart City , Intelligent Fusion Techniques , Deep Feature Learning.
[1] Li, H., Xiezhang, T., Yang, C., Deng, L., & Yi, P. (2021). Secure Video Surveillance Framework in Smart City. Sensors, 21(13), 4419.
[2] Saeed M. Aljaberi , Ahmed N. Al-Masri, Automated Deep Learning based Video Summarization Approach for Forest Fire Detection, Journal of Intelligent Systems and Internet of Things, Vol. 5 , No. 2 , (2021) : 54-61 (Doi : https://doi.org/10.54216/JISIoT.050201)
[3] Shorfuzzaman, M., Hossain, M. S., & Alhamid, M. F. (2021). Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic. Sustainable cities and society, 64, 102582.
[4] Masud, M., Muhammad, G., Alhumyani, H., Alshamrani, S.S., Cheikhrouhou, O., Ibrahim, S. and Hossain, M.S., 2020. Deep learning-based intelligent face recognition in IoT-cloud environment. Computer Communications, 152, pp.215-222.
[5] Ramprasad, L., & Amudha, G. (2014, February). Spammer detection and tagging based user generated video search system—A survey. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE.
[6] Janakiramaiah, B., Kalyani, G., & Jayalakshmi, A. (2021). Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm. Evolutionary Intelligence, 14(2), 635-642.
[7] Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 1-21.
[8] Gheisari, M., Najafabadi, H. E., Alzubi, J. A., Gao, J., Wang, G., Abbasi, A. A., & Castiglione, A. (2021). OBPP: An ontology-based framework for privacy-preserving in IoT-based smart city. Future Generation Computer Systems, 123, 1-13.
[9] Jin, Y., Qian, Z., & Yang, W. (2020). UAV cluster-based video surveillance system optimization in heterogeneous communication of smart cities. IEEE Access, 8, 55654-55664.
[10] Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing.
[11] Do, D. T., Van Nguyen, M. S., Nguyen, T. N., Li, X., & Choi, K. (2020). Enabling multiple power beacons for uplink of noma-enabled mobile edge computing in wirelessly powered IOT. IEEE Access, 8, 148892-148905.
[12] Yoon, C. S., Jung, H. S., Park, J. W., Lee, H. G., Yun, C. H., & Lee, Y. W. (2020). A cloud-based UTOPIA smart video surveillance system for smart cities. Applied Sciences, 10(18), 6572.
[13] Do, D. T., Nguyen, T. T. T., Nguyen, T. N., Li, X., & Voznak, M. (2020). Uplink and downlink NOMA transmission using full-duplex UAV. IEEE Access, 8, 164347-164364.
[14] Zahraa Faiz Hussain, & Hind Raad Ibraheem. (2023). Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection. Mesopotamian Journal of CyberSecurity, 2023, 35–39. https://doi.org/10.58496/MJCS/2023/007
[15] Nagrath, P., Thakur, N., Jain, R., Saini, D., Sharma, N. and Hemanth, J., 2022. Understanding new age of intelligent video surveillance and deeper analysis on deep learning techniques for object tracking. In IoT for Sustainable Smart Cities and Society (pp. 31-63). Cham: Springer International Publishing.
[16] Elhoseny, M. (2020). Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems. Circuits, Systems, and Signal Processing, 39(2), 611-630.
[17] Song, X. H., Wang, H. Q., Venegas-Andraca, S. E., & Abd El-Latif, A. A. (2020). Quantum video encryption based on qubit-planes controlled-XOR operations and improved logistic map. Physica A: Statistical Mechanics and its Applications, 537, 122660.
[18] Yassine, S., Kadry, S., & Sicilia, M. A. (2020). Statistical Profiles of Users’ Interactions with Videos in Large Repositories: Mining of Khan Academy Repository. KSII Transactions on Internet and Information Systems (TIIS), 14(5), 2101-2121.
[19] Kumar, S., & Soundrapandiyan, R. (2021). A multi-image hiding technique in dilated video regions based on cooperative game-theoretic approach. Journal of King Saud University-Computer and Information Sciences.
[20] Kumar, P. M., & Seon, H. C. (2021). Internet of things-based digital video intrusion for intelligent monitoring approach. Arabian Journal for Science and Engineering, 1-11.
[21] Angadi, S., & Nandyal, S. (2020). Human identification system based on spatial and temporal features in the video surveillance system. International Journal of Ambient Computing and Intelligence (IJACI), 11(3), 1-21.
[22] Feizi, A. (2020). Hierarchical detection of abnormal behaviors in video surveillance through modeling normal behaviors based on AUC maximization. Soft Computing, 24(14), 10401-10413.
[23] Appathurai, A., Sundarasekar, R., Raja, C., Alex, E. J., Palagan, C. A., & Nithya, A. (2020). An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circuits, Systems, and Signal Processing, 39(2), 734-756.
[24] Thenmozhi, T., & Kalpana, A. M. (2020). Adaptive motion estimation and sequential outline separation based moving object detection in video surveillance system. Microprocessors and Microsystems, 76, 103084. [25] Moorthy, K., Ali, M.H., Ismail, M.A., Chan, W.H., Mohamad, M.S. and Deris, S., 2019. An Evaluation of Machine Learning Algorithms for Missing Values Imputation. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(12S2).
[26] Jaber, M.M., Ali, M.H., Abd, S.K., Jassim, M.M., Alkhayyat, A., Alreda, B.A., Alkhuwaylidee, A.R. and Alyousif, S., 2022. A Machine Learning-Based Semantic Pattern Matching Model for Remote Sensing Data Registration. Journal of the Indian Society of Remote Sensing, pp.1-14.
[27] Ali, M.H., Al-Jawaheri, K., Adnan, M.M., Waheed, S.R., Kadhim, K.A. and Rahim, M.S.M., 2021, September. Review of Intrusion Detection Systems Based on Machine Learning. In 2021 4th International Iraqi Conference on Engineering Technology and Their Applications (IICETA) (pp. 195-200). IEEE.
