International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Mrs.Aruna Kumari, Nimma Himani, Kantipudi Jasmitha,, Korivi Naga Krishnaveni, Kakumanu Manikanteswari
Abstract: Traffic congestion and frequent red-light violations in urban areas require smart, automated monitoring systems. This paper presents Auto-ViTrack, an IoT-based framework for real-time red-light violation detection and license plate tracking. The system uses an ESP32 microcontroller along with infrared (IR) and ultrasonic sensors to detect unauthorized vehicle movement during the red signal phase. When a violation is detected, event data including timestamp and sensor readings is sent to the ThingSpeak cloud for remote monitoring and analysis. A Convolutional Neural Network (CNN) module helps recognize license plates to identify offending vehicles accurately. This hybrid IoT and ML architecture enables event-driven data logging, reduces redundant transmissions, and optimizes bandwidth usage. Experimental results show that Auto-ViTrack outperforms existing systems like Vision-SORT, Smart-Enforce, and IoT-VMS in detection accuracy (94%), recognition reliability (92%), and latency reduction (350 ms). This cost-effective and scalable solution improves traffic law enforcement, enhances road safety, and supports the creation of sustainable smart city infrastructure.
