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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.AITCON053
Volume:
10 |
5 |
May 2026
Pages:
1-2
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence & Machine Learning
Adarsh Institute of Technology & Research Centre, Vita
Artificial Intelligence & Machine Learning
Adarsh Institute of Technology & Research Centre, Vita
Artificial Intelligence & Machine Learning
Adarsh Institute of Technology & Research Centre, Vita
Artificial Intelligence & Machine Learning
Adarsh Institute of Technology & Research Centre, Vita
Artificial Intelligence & Machine Learning
Adarsh Institute of Technology & Research Centre, Vita
The proposed system, “Student Discipline Management System using CCTV and Machine Learning,” is designed to monitor student activities and maintain discipline within educational institutions. The system uses CCTV cameras to capture video footage of classrooms and campus areas. The captured video is processed using digital image processing and machine learning techniques to analyze student behavior. The system automatically detects unusual or undisciplined activities and records them for monitoring purposes. The collected data can be used by faculty or administration to improve discipline and ensure a better learning environment.
KEYWORDS
The Student Discipline Management System using CCTV provides an automated method for monitoring student activities within educational institutions. By integrating CCTV surveillance with image processing and machine learning techniques, the system helps detect unusual behavior and maintain discipline. The system reduces manual monitoring efforts and improves campus security. It can also assist administrators in analyzing student behavior and maintaining a better learning environment.transactions. Additionally, the system offers valuable information on sales and product performance through the data analytics module. Overall, the system improves business operations and offers customers a more satisfying shopping experience.
1.Ravi Chandra M.L., Neeha Begum S., Sai Geetha P., Murali M., and Sasi Vardhan Raju J., “Student Monitoring System Using AI and Image Processing,” Journal of Ubiquitous Computing and Communication Technologies, vol. 7, no. 4, pp. 375–393, 2025. 2. Rajat Mehta, Rashi Bhardwaj, and Prakash Ramani, “Real-Time Student Surveillance System Using Machine Learning and Computer Vision,” International Journal of Advanced Research in Computer Science. 3. Shyamasree Ghosh, Ashika B. Pemmaiah, Shruti Bharti, Gopika D., and Neha Joshy, “An Intelligent Automated Monitoring System Using Video Surveillance Based Recognition,” International Journal of Health Sciences, 2022. 4. Qingtang Liu, Xinyu Jiang, and Ruyi Jiang, “Classroom Behavior Recognition Using Computer Vision: A Systematic Review,” Sensors, vol. 25, no. 2, 2025. 5. Chethan P. J., Naveen M. S., Syed Waseem I., and Uzair Ahamad, “Student Behavior Analysis Using Deep Learning,” EasyChair Preprint 15994, 2025. 6.Ifroh Intan Sepulau, Kusumanto Kusumanto, and Nyayu Latifah Husni, “Student Behavior Monitoring System in Classroom Environment Using YOLOv8,” Indonesian Journal of Artificial Intelligence and Data Mining, 2025. 7. Wang R., Chen S., Tian G., Wang P., and Ying S., “Real-Time Classroom Student Behavior Detection Based on Improved YOLOv8s,” Scientific Reports, 2025. 8. I. Khan, A. R. Ahmad, N. Jabeur, et al., “An Artificial Intelligence Approach to Monitor Student Performance and Devise Preventive Measures,” Smart Learning Environments, 2021.
Publication Date
5/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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