Robust Approach to Vehicle Detection and Counting with Centroid-based Tracking

This study proposes a robust computer vision-based system for autonomous vehicle identification and tracking, utilizing OpenCV with Python for real-time image processing. To precisely identify cars and bikes, the system examines both individual video frames and the motion between successive frames. To separate moving vehicles, important image processing methods like segmentation, filtering, binarization, and background subtraction are used. In order to facilitate in-depth traffic analysis, the system divides vehicles into two categories: motorbikes and light vehicles. For real-time applications including toll collection, highway surveillance, and traffic monitoring, these methods enable precise vehicle classification, speed prediction, counting, and tracking. The system is perfect for urban traffic control and planning because of its non-intrusive design and strong image processing capabilities. The suggested system's flexible and scalable design provides a workable answer for real-time vehicle monitoring and identification in civilian applications, enhancing traffic safety, streamlining traffic management, and promoting more intelligent urban planning.