AI Based Surveillance System
Abstract
Increased demand for enhanced safety and security in the different sectors has brought with it great advancements in AI-powered smart surveillance systems. Such systems take advantage of the latest developments in computer vision, sensor integration, and Automated Machine Learning to monitor and analyze video and image data automatically. Unlike traditional surveillance systems, which highly rely on human intervention and often fail to provide real-time decision-making or predictive analysis, modern AI-powered systems address these limitations. Specifically, tracking individuals across multiple locations and consolidating their data into a unified framework remains a challenge for conventional setups. The system utilizes powerful AI algorithms, such as HAAR-CASCADE, for face detection. The algorithm ensures that there is accurate and reliable identification of individuals in real time. Furthermore, the system carries out behavior analysis to identify any unusual activities, hence improving its application in the public and private sectors. Multithreaded Python programming is used which allows the efficient handling of high volumes of data since several tasks, including video feed analysis and log generation, can run at the same time without delay. An ideal implementation of this system is in educational institutions, where it streamlines processes such as monitoring student behavior. With this system, upon the detection of students from various locations on campus, a single l…
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AI-based systems can track student activity across digital and physical environments simultaneously, creating unprecedented visibility into student behaviors.
“Maheshwari et al. (2025) further document that these AI-based systems can track student activity across digital and physical environments simultaneously, creating unprecedented visibility into student behaviors.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Maheshwari et al. (2025)
Cites
Face detection using representation learning
Zhan, S., Tao, Q., & Li, X. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.130 · Cited by 69 · see citing works
Event-based large scale surveillance video summarization
Song, X., Sun, L., Lei, J., Tao, D., Yuan, G., & Song, M. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.131 · Cited by 52 · see citing works
On using AI-Based Human Identification in Improving Surveillance System Efficiency
Alajrami, E., Tabash, H., Singer, Y., & Astal, M. T. O. E. (2019) · International Conference on Promising Electronic Technologies (ICPET) · doi:10.1109/icpet.2019.00024 · Cited by 26 · see citing works
Face identification with second-order pooling in single-layer networks
Shen, F., Yang, Y., Zhou, X. S., Liu, X., & Shao, J. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.133 · Cited by 12 · see citing works
Artificial Intelligence Surveillance System
Dsouza, A., Fernandes, A., Jacob, A., & Siddiqui, A. (2022) · International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · doi:10.1109/ic3sis54991.2022.9885659 · Cited by 12 · see citing works
Hasware, D. U. A. (2023) · Proceeding International Conference on Science and Engineering · doi:10.52783/cienceng.v11i1.369 · Cited by 2 · see citing works
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GİRAY, L., CANER, H. N., & AMOOZEGAR, A. (2026) · Journal of Narrative and Language Studies · doi:10.59045/nalans.2026.109
IMAGE PROCESSING AND COMPUTER VISION TECHNIQUES FOR AUTOMATED SMART SURVEILLANCE SYSTEMS
Syahlan, Z., Lim, S., & Wong, L. (2025) · Journal of Computer Science Advancements · doi:10.70177/jsca.v3i6.3323
- Authors
- Maheshwari, K. K., Hajano, S., Baloch, S., Zafar, Z., & Fatima, T.
- Affiliations
- Mehran University of Engineering and Technology (PK)
- Venue
- 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)
- Publisher
- IEEE
- Published
- 2025-04-23
- Pages
- 1-6
- OpenAlex
- W4412405476
- Cited by
- 0
- References
- 6
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- video surveillance, face detection, Haar cascade, human tracking, behavior analysis, automated machine learning, student behavior monitoring, campus safety, real-time video analysis
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