In this paper, we have addressed the problem of real-time detection and tracking of dynamic objects using quadrotors. We have developed a novel object detection algorithm by analyzing and matching the color and spacial features of the target from monocular image sequences. The proposed object detection algorithm can track the objects with high Frame Per Second (FPS) which is suitable for low-end onboard computers that are used in quad-rotors. In addition, we also estimate the position of the target object in real world so that the drone can track the object accurately. A rigorous experimental analysis is provided to show the efficacy of the proposed approach in indoor as well as outdoor environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Gaussian Mixture Model (GMM) Based Dynamic Object Detection and Tracking


    Beteiligte:
    Anand, Vishnu (Autor:in) / Pushp, Durgakant (Autor:in) / Raj, Rishin (Autor:in) / Das, Kaushik (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    1900901 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Video Object Segmentation Based on Gaussian Mixture Model

    Xiaohe, L. / Taiyi, Z. / Yatong, Z. | British Library Online Contents | 2006


    Vehicle detection and tracking using Gaussian Mixture Model and Kalman Filter

    Indrabayu / Bakti, Rizki Yusliana / Areni, Intan Sari et al. | IEEE | 2016


    Driver face tracking using Gaussian mixture model(GMM)

    Zhu, Y. / Fujimura, K. | Tema Archiv | 2003


    Gaussian Mixture PHD Filter for Space Object Tracking (AAS 13-242)

    Cheng, Y. / DeMars, K.J. / Fruh, C. et al. | British Library Conference Proceedings | 2013