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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Drone/Bird Classification Based on Features of Tracks Trajectories




    Publication date :

    2023-03-04


    Size :

    7318627 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    DEFORMABLE WING-BASED AGRICULTURAL BIONIC BIRD REPELLING DRONE

    HAN XIN / WANG HUIZHENG / LAN YUBIN et al. | European Patent Office | 2022

    Free access

    DRONE DEVICE FOR DEFENDING BIRD ACCESS

    JIN TAE SEOK / JEONG JAE HA | European Patent Office | 2024

    Free access

    BIRD: Battlefield-Integrated Reconnaissance Drone With VQA

    Rayon, Nathan / Menser, Kobi / Catalano, Louis et al. | AIAA | 2025


    AIRFIELD BIRD STRIKE SYSTEM USING ROBOT DRONE

    CHOI BYEONG GWAN | European Patent Office | 2020

    Free access

    Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios

    Paula Seoane / Enrique Aldao / Fernando Veiga-López et al. | DOAJ | 2024

    Free access