Traffic parameter extraction using video-based vehicle tracking

We propose a vehicle tracking algorithm that takes a new occlusion reasoning approach. We consider two different types of occlusions: explicit occlusion and implicit occlusion. We also propose a traffic flow extraction method with the velocity and trajectory of the moving vehicles. The proposed vehicle tracking system is composed of three parts: vehicle segmentation, vehicle tracking, and traffic parameter extraction. The vehicle segmentation part separates moving vehicles from their background. We employ the adaptive background approach, which does not update the background of moving objects. We also design a 2D token-based algorithm for vehicle tracking using Kalman filtering that has a modest amount of computational complexity. The traffic parameter extraction part estimates the traffic parameters, such as the vehicle count and the average speed. It also extracts the traffic flows. Finally, we have evaluated the proposed algorithm with some MPEG-7 test sequences