Smart CCTV-based Bike Monitoring System for College Campuses: Automated Violation Logging and Guardian Notification

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.