Vehicle detection on images from satellite using oriented fast and rotated brief

Traffic density data plays important role in traffic management, road planning as well as urban land use planning. Several efforts have been used to gather this data, mainly by detecting and counting vehicles in roads by processing images from CCTV placed in certain positions in roads. The main disadvantage of this approach is that it is only possible to detect and count vehicles effectively in a relatively limited area of the roads due to limited height and camera resolution. By using satellite images or images taken from drones, the coverage area of the roads can be increased significantly, however problems of false detection due to objects looking similar to vehicles also increases. This reseach uses Template Matching methos by using correlation equation, haar cascade classification, keypoint detection using maximally stable extremal region and Oriented FAST and Rotated BRIEF (ORB) feature extraction method. The highest recall and precision value using MSER and ORB are 100 and 75%, respectively.