Algorithm evaluation for parallel detection and tracking of UAVs

Denis Ojdanić; Christopher Naverschnigg; Andreas Sinn; Georg Schitter

doi:10.1117/12.3017037

18 June 2024 Algorithm evaluation for parallel detection and tracking of UAVs

Denis Ojdanić, Christopher Naverschnigg, Andreas Sinn, Georg Schitter

Proceedings Volume 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII; 129981F (2024) https://doi.org/10.1117/12.3017037
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

This paper presents the evaluation of object detectors and trackers within a parallel software architecture to enable long distance UAV detection and tracking in real-time using a telescope-based optical system. The architecture combines computationally expensive deep learning-based object detectors with traditional object trackers to achieve a detection and tracking rate of 100 fps. Four object detectors, FRCNN, SSD, Retinanet and FCOS, are fine-tuned on a custom UAV dataset and integrated together with three trackers, Medianow, KCF and MOSSE, into a parallel software architecture. The evaluation is conducted on a separate set of test images and videos. The combination of FRCNN and Medianow shows the best performance in terms of intersection over union and center location offset on the video test set, enabling detection and tracking of UAVs at 100 fps.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Denis Ojdanić, Christopher Naverschnigg, Andreas Sinn, and Georg Schitter "Algorithm evaluation for parallel detection and tracking of UAVs", Proc. SPIE 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII, 129981F (18 June 2024); https://doi.org/10.1117/12.3017037

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