  Detect-and-Avoid (DAA) capabilities are critical for safe operations of
unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time
vision-only detect and tracking framework that respects the size, weight, and
power (SWaP) constraints of sUAS systems. Given the low Signal-to-Noise ratios
(SNR) of far away aircraft, we propose using full resolution images in a deep
learning framework that aligns successive images to remove ego-motion. The
aligned images are then used downstream in cascaded primary and secondary
classifiers to improve detection and tracking performance on multiple metrics.
We show that AirTrack outperforms state-of-the art baselines on the Amazon
Airborne Object Tracking (AOT) Dataset. Multiple real world flight tests with a
Cessna 182 interacting with general aviation traffic and additional
near-collision flight tests with a Bell helicopter flying towards a UAS in a
controlled setting showcase that the proposed approach satisfies the newly
introduced ASTM F3442/F3442M standard for DAA. Empirical evaluations show that
our system has a probability of track of more than 95% up to a range of 700m.
Video available at https://youtu.be/H3lL_Wjxjpw .

Computer Vision and Pattern Recognition;Machine Learning;Robotics