SMod: Scene-Specific-Prior–Based Moving Object Detection for Airport Apron Surveillance Systems

The detection of moving objects in airport apron surveillance encounters serious detection defects, which reflects that the classification is biased toward the background. In this article, we propose a new method, scene-specific-prior–based moving object detection (SMod), to detect the moving object by the use of the prior knowledge specific to the airport apron. We find that the airport apron is made of concrete and has a special gray–white color distribution. Therefore, first, we use a dual-mode Gaussian distribution to fit the color distribution of the airport apron. Further analysis shows that this fitted distribution also reflects the probability of a detection defect, which occurs when the aircraft and occluded apron area share a similar color distribution. Based on this, second, we present a scene-specific prior model based on the dual-mode distribution, where pixels with a larger detection defect probability are more likely to be classified as the foreground, so that the prior model is able to bias the classification to the foreground. As a result, the detection defect in the airport apron can be compensated for. Third, the prior model is used in conjunction with an apron model and an object model to detect a moving object within a Bayesian framework. Next, we conduct comparison experiments on the airport ground video surveillance benchmark to verify the effectiveness of the proposed algorithm. Finally, we demonstrate the application of SMod in an airport apron surveillance system.