Local region statistical distance measure for tracking in Wide Area Motion Imagery

Abstract

In this paper we propose a novel tracking method in Wide Area Motion Imagery (WAMI) data based on local region histogram feature and a statistical distance measure. The aspects that make tracking particularly challenging are global camera motion, large movement of targets, poor gradient and texture information and absence of color information. Global camera motion is reduced or eliminated by registering the images from frame to frame employing SURF (Speeded Up Robust Feature). The proposed method is based on a variant of intensity histogram that encodes both spatial and intensity information. The method is evaluated on aerial WAMI data. The robustness of the feature eliminates the need for background subtraction in videos. A performance comparison of our feature descriptor with other descriptors such as HOG (Histogram of Gradients), SURF and SIFT (Scale Invariant Feature Transform) shows the effectiveness of the proposed method. We also show a comparison of our method with mean-shift tracking to show its effectiveness in tracking on WAMI data.