Communication Dans Un Congrès Année : 2023

Drone/Bird Classification Based on Features of Tracks Trajectories

Résumé

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

Fichier non déposé

Dates et versions

hal-04101996 , version 1 (22-05-2023)

Identifiants

Citer

Maksat Kengeskanov, Amal El Fallah Seghrouchni, Raed Abu Zitar, Frederic Barbaresco. Drone/Bird Classification Based on Features of Tracks Trajectories. 2023 IEEE Aerospace Conference, 2023, Big Sky, United States. pp.1-8, ⟨10.1109/AERO55745.2023.10115762⟩. ⟨hal-04101996⟩

Collections

145 Consultations
0 Téléchargements

Altmetric

Partager

  • More