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Related Experiment Video

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Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

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Benchmarking Deep Trackers on Aerial Videos.

Abu Md Niamul Taufique¹, Breton Minnehan¹, Andreas Savakis¹

¹Rochester Institute of Technology, Rochester, NY 14623, USA.

Sensors (Basel, Switzerland)

|January 23, 2020

Summary

This summary is machine-generated.

Deep learning visual object trackers perform poorly on aerial datasets due to challenges like small targets and camera motion. Performance degradation is observed compared to ground-level tracking benchmarks.

Keywords:

correlation filters deep learning siamese networks visual object tracking

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Deep learning visual object trackers excel on ground-level benchmarks.
Aerial tracking introduces unique challenges not present in ground-level scenarios.

Purpose of the Study:

To evaluate the performance of ten deep learning-based visual object trackers on aerial datasets.
To compare different tracking approaches including tracking by detection, discriminative correlation filters, Siamese networks, and reinforcement learning.

Main Methods:

Experiments were conducted on four aerial datasets: OTB2015 (aerial subset), UAV123, UAV20L, and DTB70.
Ten state-of-the-art deep learning trackers employing diverse methodologies were selected for comparison.

Main Results:

All evaluated trackers demonstrated significantly reduced performance on aerial datasets compared to ground-level videos.
Key challenges identified include smaller target size, complex camera motion, target rotation, out-of-view movement, and environmental clutter.

Conclusions:

Current deep learning trackers are not optimized for the complexities of aerial visual object tracking.
Further research is needed to develop robust trackers capable of handling aerial surveillance and navigation challenges.