HMRN: heat map regression network to detect and track small objects in wide-area motion imagery

Abstract

We propose HMRN, a deep heat map regression network to detect and track small moving objects in wide-area motion imagery (WAMI) by modifying a deep multi-object tracker. Object detection in WAMI images is challenging, because they cover large geographical areas and contain many small vehicles that do not have sufficient appearance-based cues for effective detection. Typically, background subtraction is applied to detect changed regions in WAMI image sequences. However, these methods suffer from high number of false detections. In this paper, we represent objects in WAMI images as heat maps and develop a deep regression network that predicts the object heat maps from current image, previous image and the predicted heat map of the previous image. Experiments are performed on Wright–Patterson Air Force Base (WPAFB) 2009 dataset and results show that the proposed method is almost ten times faster than its competitors while achieving state-of-the-art detection and tracking accuracy as well. We achieve significant reduction in false positives leading to an increase in average precision and F1 scores.