A Way to Automatically Generate Lane Level Traffic Data from Video in the Intersections

Abstract

Lane level traffic data such as average waiting time and flow data at each turn direction not only enable navigation systems to provide users with more detailed and finer-grained information; it can also pave the way for future traffic congestion prediction. Although few studies considered extracting traffic flow data from a video at the lane level, it is not clear how many vehicles required turn left in fine-grained lanes during a fixed time. Many previous works focus on applying sensor data instead to videos to extract traffic flow. However, the reversible lanes and various shooting angles obstruct the progress of constructing a traffic data collection system. A framework is proposed to get these data in the intersection directly from a video and solve the problem of vehicle occlusion based on the delayed matching model. First, the different direction lanes are detected automatically by clustering trajectory data which are generated by tracking each vehicle. Experiments are conducted on urban intersections to show that our method can generate these traffic data effectively.