A real-time multiple vehicle classification and tracking system with occlusion handling

Abstract

In this paper, we propose a new traffic surveillance system with the ability to perform surveillance tasks in real time. The proposed classification method is able to classify objects into vehicles and non-vehicles (pedestrians and motorcycles). In addition, the system can detect the type of vehicle as large or small efficiently, without considering size-based features. Our tracking algorithm uses a region-based tracker to explicitly define occlusion relationships between vehicles. For occlusion handling, we use a Kalman filter to estimate the position of moving vehicles and a tree structure by which moving regions are arranged in a tree. In this way, we obtain robust motion estimates and trajectories for vehicles, even in presence of occlusions. We show the efficient performance of the proposed system in some experiments with real world traffic scenes.