Vehicle Flow Statistics System in Video Surveillance based on Camshift and Kalman Filter

With the rapid development of the modern economy, transportation safety and security are becoming critical. The standards for the management of transportation systems are also becoming higher and higher. The modern real-time control technology is integrated into the real-time control of the intelligent traffic system. This paper establishes a smart transportation system that uses traffic pavement cameras to detect traffic flow in a specific period. The system used the background difference method based on the Gaussian model and the Camshift algorithm based on the Kalman filter. It is mainly divided into background modeling, foreground detection, vehicle tracking, and vehicle flow calculation. After background detection, remove the background parts and then use foreground detection to accurately extract different vehicle types of various models that need to be processed. Through tracking and scientific statistics of the detected targets, real-time traffic flow data is calculated. The traffic flow changes are intuitively fed back to the system users in a line chart. Thus significantly reduce the manual part's workload, effectively improve the efficiency and effectiveness of traffic management.