Wide area motion imagery tracking

Abstract

Automated target tracking with wide area motion imagery (WAMI) presents significant challenges due to the low resolution, low framerate data provided by the sensing platform. This paper discusses many of these challenges with a focus on the use of features to aid the tracking process. Results illustrate the potential benefits obtained when combining target kinematic and feature data, but also demonstrate the difficulties encountered when tracking low contrast targets, targets that have appearance models similar to their background and under conditions where traffic density is relatively high. Other difficulties include target occlusion and move-stop-move events, which are mitigated with a new composite detection method that seamlessly integrates feature and kinematic data. A real WAMI dataset was used in this study, and specific vignettes will be discussed. A single target tracker is implemented to demonstrate the concepts and provide results.