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Multi-Object Tracking in Drone Videos

 
: Stadler, Daniel

:
Fulltext urn:nbn:de:0011-n-6383771 (2.0 MByte PDF)
MD5 Fingerprint: 1c461769bc9e0dd3d25d3e29f67c8886
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Created on: 28.7.2021


Beyerer, Jürgen (Ed.); Zander, Tim (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2020. Proceedings : 27th to the 31st of July 2020, Karlsruhe
Karlsruhe: KIT Scientific Publishing, 2021 (Karlsruher Schriften zur Anthropomatik 51)
ISBN: 978-3-7315-1091-8
DOI: 10.5445/KSP/1000130397
pp.123-133
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2020, Karlsruhe>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
In this report, three popular methods for multi-pedestrian tracking are extended to a multi-category setting and tested on a large drone-based dataset. A thorough comparison of the algorithms is presented and a common shortcoming is identified. Building on this, a new tracking-by-detection based approach is developed that outperforms the other methods by a large margin. In addition, a state-of-the-art object detection model is adapted for the drone imagery, since no public detections are available for the dataset.

: http://publica.fraunhofer.de/documents/N-638377.html