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Wide Area Motion Imagery (WAMI) yields high-resolution images with a largenumber of extremely small objects. Target objects have large spatialdisplacements throughout consecutive frames. This nature of WAMI images makesobject tracking and detection challenging. In this paper, we present our deepneural network-based combined object detection and tracking model, namely, HeatMap Network (HM-Net). HM-Net is significantly faster than state-of-the-artframe differencing and background subtraction-based methods, withoutcompromising detection and tracking performances. HM-Net follows the objectcenter-based joint detection and tracking paradigm. Simple heat map-basedpredictions support an unlimited number of simultaneous detections. Theproposed method uses two consecutive frames and the object detection heat mapobtained from the previous frame as input, which helps HM-Net monitorspatio-temporal changes between frames and keeps track of previously predictedobjects. Although reuse of prior object detection heat map acts as a vitalfeedback-based memory element, it can lead to an unintended surge offalse-positive detections. To increase the robustness of the method againstfalse positives and to eliminate low confidence detections, HM-Net employsnovel feedback filters and advanced data augmentations. HM-Net outperformsstate-of-the-art WAMI moving object detection and tracking methods on the WPAFBdataset with its 96.2% F1 and 94.4% mAP detection scores while achieving a61.8% mAP tracking score on the same dataset. This performance corresponds toan improvement of 2.1% for F1, 6.1% for mAP scores on detection, and 9.5% formAP score on tracking over the state-of-the-art.

HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery