| Issue |
EPJ Web Conf.
Volume 363, 2026
International Conference on Low-Carbon Development and Materials for Solar Energy (ICLDMS’26)
|
|
|---|---|---|
| Article Number | 02004 | |
| Number of page(s) | 12 | |
| Section | Engineering Materials | |
| DOI | https://doi.org/10.1051/epjconf/202636302004 | |
| Published online | 16 April 2026 | |
https://doi.org/10.1051/epjconf/202636302004
Smart CCTV-based Bike Monitoring System for College Campuses: Automated Violation Logging and Guardian Notification
Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 16 April 2026
Abstract
Increasing usage of two-wheelers in colleges has brought a lot of safety challenges; non-compliance of wearing helmets, three-wheel riding and unauthorised movement of vehicles, thus the need for intelligent surveillance systems. Traditional surveillance methods are highly dependent on manual monitoring or rule-based vision techniques and thus lack timely response, high manpower consumption and low adaptability to dynamic environmental conditions. To overcome these limitations, a Smart CCTV based Bike Monitoring System (BMS) for detecting and analyzing the violations is proposed and it combines deep learning based detecting the presence of riders, multi-violation analysis, License Plate Recognition (LPR), Real-time Violation Logging and automatic notification of guardians. The proposed system takes live video streams and accurately identifies violations and match to registered vehicles without human intervention. Experimental results show that the detection accuracy achieves 98.9%, the false logging rate is reduced, real-time processing speed is over 40 FPS and notification delay is less than 5 seconds. These results show better reliability, scalability and efficiency towards smart campus traffic safety management.
Key words: Smart campus traffic surveillance / Two-wheeler violation analytics / Helmet and triple-riding detection / Deep learning-based rider monitoring / CCTV-driven violation logging
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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