Enhanced Dynamic Vehicle Detection and Tracking to Improve the Quality of Image Processing Using Deep Learning

One of the most crucial duties in varied traffic scenarios is vehicle recognition and categorization. Vehicle detection is the first step in tracking and counting vehicles. This article uses a deep learning algorithm for vehicle detection and classification. Mask the RCNN model for deep learning used for recognizing numerous variety cars. The automobiles are numbered and monitored with a Hungarian algorithm plus a Kalman filter. The Hungarian technique moves an element from one frame to the next. After we identified detection to be connected with a tracker, the present state is predicted using a Kalman filter method, and we update the state depending on bounding box measurement. Construct and test vehicle monitoring and counting methods using the AAU-Rainsnow dataset while testing car recognition and classification techniques using the AAU-Rainsnow dataset and IITM-Hetra dataset. The results of the experiments show that tracking vehicles via detection outperforms traditional methods of monitoring and counting vehicles.