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6 papers · 1 filter
MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking
Kenneth Rapko, Wanlin Xie, Andrew Walsh
Evaluating tracking model performance is a complicated task, particularly for non-contiguous, multi-object trackers that are crucial in defense applications. While there are variou…
Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications
Wanlin Xie, Jaime Ide, Daniel Izadi +7
Multi-object tracking (MOT) is a crucial component of situational awareness in military defense applications. With the growing use of unmanned aerial systems (UASs), MOT methods fo…
Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features
Michael Harner, Austen Groener, Mark Pritt
In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel…
A Comparison of Deep Learning Object Detection Models for Satellite Imagery
Austen Groener, Gary Chern, Mark Pritt
In this work, we compare the detection accuracy and speed of several state-of-the-art models for the task of detecting oil and gas fracking wells and small cars in commercial elect…
Globally-scalable Automated Target Recognition (GATR)
Gary Chern, Austen Groener, Michael Harner +4
GATR (Globally-scalable Automated Target Recognition) is a Lockheed Martin software system for real-time object detection and classification in satellite imagery on a worldwide bas…
A spectral optical flow method for determining velocities from digital imagery
Neal Hurlburt, Steve Jaffey
We present a method for determining surface flows from solar images based upon optical flow techniques. We apply the method to sets of images obtained by a variety of solar imagers…