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Visual Object Tracking On Otb 2015

Metrics

AUC

Results

Performance results of various models on this benchmark

		Paper Title	Repository
ODTrack-L	0.724	ODTrack: Online Dense Temporal Token Learning for Visual Tracking
ODTrack-B	0.723	ODTrack: Online Dense Temporal Token Learning for Visual Tracking
STMTrack	0.719	STMTrack: Template-free Visual Tracking with Space-time Memory Networks
SAMURAI-L	0.715	SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
PiVOT-L	0.712	Improving Visual Object Tracking through Visual Prompting
HIPTrack	0.71	HIPTrack: Visual Tracking with Historical Prompts
KeepTrack	0.709	Learning Target Candidate Association to Keep Track of What Not to Track
TRASFUST	0.701	Tracking-by-Trackers with a Distilled and Reinforced Model
AAA	0.70	AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee
ASRCF	0.692	Visual Tracking via Adaptive Spatially-Regularized Correlation Filters	-
SeqTrack-L384	0.683	Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
SE-SiamFC	0.66	Scale Equivariance Improves Siamese Tracking
SA-Siam	0.657	A Twofold Siamese Network for Real-Time Object Tracking
SiamVGG	0.654	SiamVGG: Visual Tracking using Deeper Siamese Networks
UniTrack_DCF	0.618	Do Different Tracking Tasks Require Different Appearance Models?
CFNet	0.568	End-to-end representation learning for Correlation Filter based tracking	-
GradNet	-	GradNet: Gradient-Guided Network for Visual Object Tracking

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Visual Object Tracking On Otb 2015 | SOTA | HyperAI