A Smart Camera System for Vehicle Tracking and Traffic Flow Analysis Using Segmentation

Abstract

The paper introduces a modern algorithm tailored for monitoring traffic dynamics and using stationary video sensors focused on unique street segments. Based on segmentation techniques, this algorithm efficiently tracks motors within lanes and is proper for deployment on smart digital camera systems. Empirical investigations demonstrate its effectiveness in extensively decreasing false detections and times of undetected vehicles compared to present tactics. Furthermore, the algorithm complements precision in estimating vehicle dimensions and velocities by lowering object category errors. The experimental studies also monitor insights into its robustness across varying site visitor conditions, highlighting its realistic significance in enhancing road protection and site visitor efficiency, together with its ability for seamless integration with present site visitor management structures. Traffic signal lights can be optimized using vehicle flow statistics obtained from the Smart Camera Surveillance System. This research focuses on creating an efficient traffic control system by detecting and counting the number of vehicles at various times and locations. One of the most significant problems in major cities worldwide is traffic congestion during peak hours, such as office hours and breaks. Often, traffic signals remain green even when no vehicles are approaching, or long queues of cars wait despite an empty road due to improper traffic signal timing. Our developed smart camera system can address these issues by adjusting vehicle passing times based on real-time data.