Title:
Computer Vision and Deep Learning Based Approach for Traffic Violations Due To Over-Speeding and Wrong Direction Detection
Authors:
Shailendra Singh Kathait: Co-Founder and Chief Data Scientist, Ashish Kumar: Principal Data Scientist, Samay Sawal: Intern Data Scientist, Ram Patidar: Data Scientist, Khushi Agrawal: Intern Data Scientist [all Valiance Solutions Noida, India]
Summary:
The research paper titled “Computer Vision and Deep Learning based Approach for Traffic Violations due to Over-speeding and Wrong Direction Detection” presents a cost-effective and scalable method for monitoring traffic violations using non-specialized public cameras. Traditional traffic enforcement relies on expensive infrastructure like Automatic Number Plate Recognition (ANPR) cameras and radar guns, which are often limited to specific locations due to their high costs. In contrast, this study leverages widely available public surveillance cameras, repurposing them for traffic monitoring without significant additional investments.
The proposed system integrates state-of-the-art deep learning object detection models, specifically YOLO (You Only Look Once) architectures, with advanced computer vision techniques to accurately estimate vehicle speed and detect direction in real-time. By analyzing video feeds, the system identifies vehicles, tracks their movements, calculates speeds, and determines travel directions. This enables the detection of critical traffic violations such as over-speeding and wrong-direction driving.
Experimental results demonstrate the robustness, accuracy, and real-time capabilities of the approach, highlighting its potential for practical deployment in urban traffic surveillance. The modular design and reliance on general-purpose cameras facilitate widespread and affordable implementation, offering a viable solution for enhancing traffic law enforcement and road safety in rapidly urbanizing areas.
