Drone/Bird Classification Based on Features of Tracks Trajectories
Document Type
Conference Proceeding
Publication Title
IEEE Aerospace Conference Proceedings
Abstract
This paper presents the outcome of several machine learning techniques used for the task of bird/drone classification based on their tracks. Instead of using static images, the dynamics and features extracted from the trajectories captured in videos are used to provide a more accurate and reliable recognition task. Standard Machine Learning methods such as SVM and Random Forest are used for learning this classification. Features based on the kinematics, Gabor filter, and Gray Level Co-occurrence Matrix are utilized. Several comparisons and experiments based on benchmark data sets are shown.
DOI
10.1109/AERO55745.2023.10115762
Publication Date
1-1-2023
Recommended Citation
M. Kengeskanov et al., "Drone/Bird Classification Based on Features of Tracks Trajectories," IEEE Aerospace Conference Proceedings, vol. 2023-March, Jan 2023.
The definitive version is available at https://doi.org/10.1109/AERO55745.2023.10115762