Vehicle Classification in Motorized Traffic Analysis Using CNN

Over the recent decade, the auto-influx pressure on existing infrastructure and traffic management systems has been substantial. There are over a billion active vehicles worldwide, and in India alone, there are an estimated 60-70 million. Most cities in India witness unprecedented congestion and parking problems during rush hour. Hence, classifying type of vehicles correctly on busy streets is fundamental to understanding traffic flow and adopting data-driven approaches in improving transportation infrastructure. The premise is that these technologies, aided by deep-learning models, can disrupt existing paradigms such as real-time traffic surveillance, automatic toll management, and law enforcement. Hence, as part of the ITS, we have employed a CNN-based vehicle classification system for effective real-time image processing, thanks to the constant improvements in computer vision. Modern image-processing and pattern recognition techniques have established a solid foundation for further applications in automated toll collection, real-time traffic enforcement, and monitoring. Thus, the key contribution of this CNN model is to classify vehicles detected in the surveillance image across different types: trucks, motorcycles, buses, vans, and articulated trucks. This classification would result in much smarter highway monitoring, detection of traffic violations, accident prevention, and stopping unauthorized vehicles from surveillance cameras. Proper eco-scheduling of traffic management that is based on real-time data will help authorities ease congestion and improve mobility in cities.