This study investigates the accurate extraction of vehicle traffic parameters in road traffic scenarios. A trajectory-based method is proposed. The method uses pan-tilt cameras in road surveillance to capture vehicle trajectories. It combines automated processing with analytical techniques to extract key parameters such as speed, flow, and density. Advanced image processing is used to detect and track vehicles continuously in video frames. Based on trajectory data, direction-specific counting lines are generated automatically. Instantaneous and average speeds are calculated using displacement and time. Traffic density is estimated through time occupancy. Experimental results show that the method achieves high accuracy in traffic volume estimation. It effectively reduces false detections and handles occlusion of small vehicles by larger ones. The method is stable and adaptable in various complex road scenarios. It provides an efficient and precise tool for traffic authorities to assess road conditions, support data-driven management strategies, and improve road capacity and safety