Automatic detection of vehicles at road intersections using a compact 3D Velodyne sensor mounted on traffic signals

Real-time traffic monitoring can play an important role in efficient traffic management and increasing road capacity. In this paper, we present a new method for automatic detection of vehicles using a compact 3D Velodyne sensor mounted on traffic signals in the urban environment. Different aspects of the new Velodyne sensor are first studied and its data are characterized for its effective utilization for our application. The sensor is then mounted on top of a traffic signal to detect vehicles at road intersections. The 3D point cloud obtained from the sensor is first over-segmented into super-voxels and then objects are extracted using a Link-Chain method. The segmented objects are then detected/classified as vehicles or non-vehicles using geometrical models and local descriptors. The results evaluated on real data not only demonstrate the efficacy but also the suitability of the proposed solution for such traffic monitoring applications.