Robust and fast detection of moving vehicles in aerial videos using sliding windows

The detection of vehicles driving on busy urban streets in videos acquired by airborne cameras is challenging due to the large distance between camera and vehicles, simultaneous vehicle and camera motion, shadows, or low contrast due to weak illumination. However, it is an important processing step for applications such as automatic traffic monitoring, detection of abnormal behaviour, border protection, or surveillance of restricted areas. In contrast to commonly applied object segmentation methods based on background subtraction or frame differencing, we detect moving vehicles using the combination of a track-before-detect (TBD) approach and machine learning: an AdaBoost classifier learns the appearance of vehicles in low resolution and is applied within a sliding window algorithm to detect vehicles inside a region of interest determined by the TBD approach. Our main contribution lies in the identification, optimization, and evaluation of the most important parameters to achieve both high detection rates and real-time processing.