AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
- URL: http://arxiv.org/abs/2208.06888v1
- Date: Sun, 14 Aug 2022 17:49:37 GMT
- Title: AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
- Authors: Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer,
Akshay Dudhane, Martin Danelljan, Hisham Cholakkal, Salman Khan, Luc Van
Gool, Fahad Shahbaz Khan
- Abstract summary: AVisT is a benchmark for visual tracking in diverse scenarios with adverse visibility.
AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios.
We benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes.
- Score: 125.77396380698639
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: One of the key factors behind the recent success in visual tracking is the
availability of dedicated benchmarks. While being greatly benefiting to the
tracking research, existing benchmarks do not pose the same difficulty as
before with recent trackers achieving higher performance mainly due to (i) the
introduction of more sophisticated transformers-based methods and (ii) the lack
of diverse scenarios with adverse visibility such as, severe weather
conditions, camouflage and imaging effects.
We introduce AVisT, a dedicated benchmark for visual tracking in diverse
scenarios with adverse visibility. AVisT comprises 120 challenging sequences
with 80k annotated frames, spanning 18 diverse scenarios broadly grouped into
five attributes with 42 object categories. The key contribution of AVisT is
diverse and challenging scenarios covering severe weather conditions such as,
dense fog, heavy rain and sandstorm; obstruction effects including, fire, sun
glare and splashing water; adverse imaging effects such as, low-light; target
effects including, small targets and distractor objects along with camouflage.
We further benchmark 17 popular and recent trackers on AVisT with detailed
analysis of their tracking performance across attributes, demonstrating a big
room for improvement in performance. We believe that AVisT can greatly benefit
the tracking community by complementing the existing benchmarks, in developing
new creative tracking solutions in order to continue pushing the boundaries of
the state-of-the-art. Our dataset along with the complete tracking performance
evaluation is available at: https://github.com/visionml/pytracking
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