@inproceedings{10.1145/3674746.3674795,
author = {Cabrera, Juan Jos'{e} and Gil, Arturo and Pay'{a}, Luis and Santo, Antonio and Reinoso, Oscar and Rodr'{i}guez, David},
title = {Detection of UAVs on a collision course using optical flow},
year = {2024},
isbn = {9798400716782},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3674746.3674795},
doi = {10.1145/3674746.3674795},
abstract = {This paper presents a method to detect, track and predict a potential collision with UAVs using an aircraft equipped with a single camera. The method analyses the movement in the camera’s image plane by means of sparse optical flow. In this way, the camera’s own movement can be modelled and cancelled by estimating a homography matrix from a set of corresponding points. Once the movement caused by the camera is cancelled other moving objects can be isolated and the presence of other UAVs can be detected. Additionally, the method predicts potential collisions by examining the alignment between the position and velocity vectors of the UAV, which are estimated up to a scale factor. The proposed method is effective at detecting and predicting collisions with UAVs, regardless of their appearance, size, or movement, making it useful for applications related to airspace security.},
booktitle = {Proceedings of the 2024 4th International Conference on Robotics and Control Engineering},
pages = {138–144},
numpages = {7},
keywords = {UAV detection, collision prediction, optical flow},
location = {Edinburgh, United Kingdom},
series = {RobCE '24}
}