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Paper ID:10.55041/IJSREM.AITCON053

Student Discipline Management through CCTV

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM.AITCON053

Volume:

10 |

5 |

May 2026

Pages:

1-2

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem.AITCON053

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.

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Authors

KS

Kshitij S.Suryawanshi

Artificial Intelligence & Machine Learning

Adarsh Institute of Technology & Research Centre, Vita

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RR

Rohit R.Hatkar

Artificial Intelligence & Machine Learning

Adarsh Institute of Technology & Research Centre, Vita

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AA

Akhilesh A.Jagtap

Artificial Intelligence & Machine Learning

Adarsh Institute of Technology & Research Centre, Vita

Download Co-Author Certificate
MS

Mrs S.M.Lokhande

Artificial Intelligence & Machine Learning

Adarsh Institute of Technology & Research Centre, Vita

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MS

Mrunal S.Salunkhe

Artificial Intelligence & Machine Learning

Adarsh Institute of Technology & Research Centre, Vita

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Abstract

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

Key Words : CCTV Monitoring Machine Learning Student Discipline System Image Processing Video Surveillance

Cite This Article

Kshitij S.Suryawanshi, Rohit R.Hatkar, Akhilesh A.Jagtap, Mrs S.M.Lokhande, Mrunal S.Salunkhe, "Student Discipline Management through CCTV," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-2, May 2026. https://doi.org/10.55041/ijsrem.AITCON053

Full Article/Conclusion

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.

References

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.

Editorial & Review Transparency

Publication Date

5/5/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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1

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3

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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