A comprehensive study on object proposals methods for vehicle detection in aerial images

Detecting vehicles in aerial images is an important task in many applications such as traffic monitoring or screening of large areas. In general, vehicle detection in aerial images is performed by applying classifiers or a cascade of classifiers within a sliding window algorithm. However, detecting vehicles in a real-time system is limited by the huge number of windows to classify, especially in case of varying object scales, aspect ratios or object orientations. To reduce the high number of windows, we propose to apply so called object proposals methods. In recent years, several object proposals methods have been proposed for generating candidate windows in detection frameworks. However, aerial images differ considerably from datasets that are typically used for exploring such methods. To examine the applicability of such methods for aerial images, we evaluate 11 state-of-the-art object proposals methods on the publicly available DLR 3K Munich Vehicle Aerial Image Dataset. First, we manually modified the provided ground truth data to enable comparison to the generated object proposals. To compensate for the differing characteristics of the aerial images, we adapted seven methods by examining different parameter settings and extensions for each method separately. Finally, we demonstrate the potential of such methods for a detection framework for aerial images as significantly fewer candidate windows are generated in comparison to sliding window.