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One of the key factors behind the recent success in visual tracking is theavailability of dedicated benchmarks. While being greatly benefiting to thetracking research, existing benchmarks do not pose the same difficulty asbefore with recent trackers achieving higher performance mainly due to (i) theintroduction of more sophisticated transformers-based methods and (ii) the lackof diverse scenarios with adverse visibility such as, severe weatherconditions, camouflage and imaging effects.  We introduce AVisT, a dedicated benchmark for visual tracking in diversescenarios with adverse visibility. AVisT comprises 120 challenging sequenceswith 80k annotated frames, spanning 18 diverse scenarios broadly grouped intofive attributes with 42 object categories. The key contribution of AVisT isdiverse and challenging scenarios covering severe weather conditions such as,dense fog, heavy rain and sandstorm; obstruction effects including, fire, sunglare and splashing water; adverse imaging effects such as, low-light; targeteffects including, small targets and distractor objects along with camouflage.We further benchmark 17 popular and recent trackers on AVisT with detailedanalysis of their tracking performance across attributes, demonstrating a bigroom for improvement in performance. We believe that AVisT can greatly benefitthe tracking community by complementing the existing benchmarks, in developingnew creative tracking solutions in order to continue pushing the boundaries ofthe state-of-the-art. Our dataset along with the complete tracking performanceevaluation is available at: https://github.com/visionml/pytracking

AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility