Real-Time Traffic Monitoring with Occlusion Handling

Traffic surveillance through vision systems is a highly demanded task. To solve it, it is necessary to combine detection and tracking in a way that meets the requirements of operating in real time while being robust against occlusions. This paper proposes a traffic monitoring system that meets these requirements. It is formed by a deep learning-based detector, tracking through a combination of Discriminative Correlation Filter and a Kalman Filter, and data association based on the Hungarian method. The viability of the system has been proved for roundabout input/output analysis with near 1,000 vehicles in real-life scenarios.