Benchmarking Deep Trackers on Aerial Videos
Rochester Institute of Technology, Rochester, NY 14623, USA
*
Author to whom correspondence should be addressed.
†
Current address: Air Force Research Laboratory, WPAFB, OH 45433, USA.
Sensors 2020, 20(2), 547; https://doi.org/10.3390/s20020547
Received: 14 December 2019
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Revised: 10 January 2020
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Accepted: 10 January 2020
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Published: 19 January 2020
(This article belongs to the Special Issue Aerial Vision and Sensors)
In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.
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MDPI and ACS Style
Taufique, A.M.N.; Minnehan, B.; Savakis, A. Benchmarking Deep Trackers on Aerial Videos. Sensors 2020, 20, 547. https://doi.org/10.3390/s20020547
AMA Style
Taufique AMN, Minnehan B, Savakis A. Benchmarking Deep Trackers on Aerial Videos. Sensors. 2020; 20(2):547. https://doi.org/10.3390/s20020547
Chicago/Turabian StyleTaufique, Abu M.N., Breton Minnehan, and Andreas Savakis. 2020. "Benchmarking Deep Trackers on Aerial Videos" Sensors 20, no. 2: 547. https://doi.org/10.3390/s20020547
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