A framework for moving target detection, recognition and tracking in UAV videos

Abstract

In this paper, we present a compound framework for moving target detection, recognition and tracking based on different altitude UAV-captured videos. The novel idea of "Divide and Merge" included in our framework is expressed as follows. Firstly, we detect the small and slow moving targets using forward-backward MHI. Secondly, two distinct tracking algorithms, Particle Filter and Mean Shift, are applied to track moving targets in different altitude UAV-captured videos. Then, recognition module divides into two classes: instance recognition and category recognition. The former identifies the target, which is occluded by trees or buildings and reappears later, and the latter classifies the detected target into one category by HoG-based SVM classifier. Besides, recognition-based abnormal target detection and clustering-based abnormal trajectory detection are added to our framework. Armed with this framework, the moving targets can be tracked in real-time and the recognized target or abnormal trajectory gives the alarm in seconds. © 2012 Springer-Verlag GmbH.