Options
Drone/Bird Classification Based on Features of Tracks Trajectories
Journal
2023 IEEE Aerospace Conference
Date Issued
2023
Author(s)
Kengeskanov, Maksat
Seghrouchni, Amal El Fallah
Barbaresco, Frederic
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 show
Scopus© citations
0
Acquisition Date
Sep 7, 2024
Sep 7, 2024