Tracking small targets in wide area motion imagery data

Abstract

Object tracking in aerial imagery is of immense interest to the wide area surveillance community. In this paper, we propose a method to track very small targets such as pedestrians in AFRL Columbus Large Image Format (CLIF) Wide Area Motion Imagery (WAMI) data. Extremely small target sizes, combined with low frame rates and significant view changes, make tracking a very challenging task in WAMI data. Two problems should be tackled for object tracking frame registration and feature extraction. We employ SURF for frame registration. Although there are several feature extraction methods that work reasonably well when the scene is of high resolution, most methods fail when the resolution is very low. In our approach, we represent the target as a collection of intensity histograms and use a robust statistical distance to distinguish between the target and the background. We divide the object into m ×n regions and compute the normalized intensity histogram in each region to build a histogram matrix. The features can be compared using the histogram comparison techniques. For tracking, we use a combination of a bearing-only Kalman filter and the proposed feature extraction technique. The problem of template drift is solved by further localizing the target with a blob detection algorithm. The new template is taken as the detected blob. We show the robustness of the algorithm by giving a comparison of feature extraction part of our method with other feature extraction methods like SURF, SIFT and HoG and tracking part with mean-shift tracking.