Object Tracking and Classification in Aerial Videos

Abstract

In the intelligence community, aerial video has become one of the fastest growing data sources and it has been extensively used in intelligence, surveillance, reconnaissance, tactical and security applications. This paper presents a tracking approach to detect moving vehicles and person in such videos taken from aerial platform. In our approach, we combine the layer segmentation approach with background stabilization and post-tracking refinement to reliably detect small moving objects at the relatively low processing speed. For each individual moving object, a corresponding layer is created to maintain an independent appearance and motion model during the tracking process. After the online tracking process, we apply a post-tracking refinement process to link the track fragments into a long consistent track ID to further reduce false alarm and increase detection rate. Furthermore, a vehicle and person classifier is also integrated into the approach to identify the moving object categories. The classifier is based on image histogram of gradient (HOG), which is more reliable to illumination variation or camera automatic gain change. Finally, we report the results of our algorithms on a large scale of EO and IR data set collected from VIVID program, and the results show that our approach achieved a good and stable tracking performance on the data set that is more than eight hours.