Fast Vehicle Detection and Tracking in Aerial Image Bursts

Caused by the rising interest in traffic surveillance for simulations and decision management many publications concentrate on automatic vehicle detection or tracking. Quantities and velocities of different car classes form the data basis for almost every traffic model. Especially during mass events or disasters a wide-area traffic monitoring on demand is needed which can only be provided by airborne systems. This means a massive amount of image information to be handled. In this paper we present a combination of vehicle detection and tracking which is adapted to the special restrictions given on image size and flow but nevertheless yields reliable information about the traffic situation. Combining a set of modified edge filters it is possible to detect cars of different sizes and orientations with minimum computing effort, if some a priori information about the street network is used. The found vehicles are tracked between two consecutive images by an algorithm using Singular Value Decomposition. Concerning their distance and correlation the features are assigned pairwise with respect to their global positioning among each other. Choosing only the best correlating assignments it is possible to compute reliable values for the average velocities