Vehicle detection from aerial color imagery and airborne LiDAR data
Yansong Liu, Sildomar T. Monteiro, Eli S. Saber · 2016
Vehicle detection and recognition from aerial imagery provides useful information for local vehicle volume estimation and traffic monitoring. In this paper, we propose a method that accurately detects vehicles in urban environment using a probabilistic classification method followed by a refinement based on object segments. Both classification and segmentation methods make use of coregistered aerial RGB images and airborne LiDAR data. Pixel-wise vehicle probability estimation is achieved using Gaussian process (GP) classification and object segments are obtained by applying a gradient based segmentation algorithm (GSEG). The vehicle is then detected by refining the initial probability estimation with the following constraints: car size, statistical significance and 3D surface shape. Experimental results show our method achieves 90.8% precision and 93.7% recall, which outperforms the ones that only use size constraints.