Airborne vehicle detection using SURF-descriptors and Support Vector Machines

Caused by the rising interest on traffic surveillance for simulations and decision management many publications focus on automatic vehicle detection systems. Vehicle counts and velocities of different car classes are the essential data basis for almost every traffic model. Especially during mass events or catastrophes conventional detection systems do not meet the demands. Thus a more flexible detector has to be used like an airborne camera system. In this paper a combination of a fast edge-based hypothesis generation and a more reliable hypothesis verification using a Support Vector Machine is presented. Due to image sizes of more than 20 megapixels at first the region of interest has to be preselected using a street database. Afterwards the first detection stage of the algorithm generates object hypotheses using especially shaped edge filters. The second detection stage verifies them by extracting the SURF-descriptor of each hypothesis. A Support Vector Machine is used to decide whether the object's descriptor represents a vehicle. It will be shown how the verification stage improves the detection reliability by discarding false positives while preselection and hypothesis generation provides less computation time.