We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. Solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach to motion stabilization of local image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As the problem is relatively new, we collected two challenging datasets for UAVs and Aircrafts, which can be used as benchmarks for flying objects detection and vision-guided collision avoidance.
@article{arxiv.1411.7715,
title = {Flying Objects Detection from a Single Moving Camera},
author = {Artem Rozantsev and Vincent Lepetit and Pascal Fua},
journal= {arXiv preprint arXiv:1411.7715},
year = {2015}
}