Airborne Moving Vehicle Detection for Video Surveillance of Urban Traffic

Urban traffic surveillance, which is designed to improve traffic management, is an important part of intelligent traffic system (ITS). In particular, airborne moving vehicle detection has become a new but hot research area since its wide view and low cost. However, airborne urban traffic surveillance is impacted by many difficulties such as camera vibration, vehicle congestion, background variance, serious thermal noise etc. Therefore, image subtraction and thermal image processing have low detection rate, while the optical flow method cannot meet the real-time application. In this paper, we propose a coarse-to-fine method, which can be divided into two stages of pre-processing and classification inspection. In pre-processing stage, the candidates regions of moving vehicle are obtained by employing Road Detection, Removal of Non-vehicle Regions and Moving Regions Extraction. The speed of this stage is fast but there is still relatively high false-positive-rate. In classification inspection stage, a well-trained cascade classifier, which refines the candidate regions, is designed to maintain a higher detection rate and a lower false alarm rate. Experimental results demonstrate that compared with representative algorithms, our method reach better performance in detection rate and false-positive-rate, while meeting the needs of real-time application.