Tracking All Road Users at Multimodal Urban Traffic Intersections

Abstract

Because of the large variability of road user appearance in an urban setting, it is very challenging to track all of them with the purpose of obtaining precise and reliable trajectories. However, obtaining the trajectories of the various road users is very useful for many transportation applications. It is particularly essential for any task that requires higher level behavior interpretation, including new safety diagnosis methods that rely on the observation of road user interactions without a collision and therefore do not require waiting for collisions to happen. In this paper, we propose a tracking method that has been specifically designed to track the various road users that may be encountered in an urban environment. Since road users have very diverse shapes and appearances, our proposed method starts from background subtraction to extract the potential a priori unknown road users. Each of these road users is then tracked using a collection of keypoints inside the detected foreground regions, which allows the interpolation of object locations even during object merges or occlusions. A finite state machine handles fragmentation, splitting, and merging of the road users to correct and improve the resulting object trajectories. The proposed tracker was tested on several urban intersection videos and is shown to outperform an existing reference tracker used in transportation research.