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Visual Object Tracking On Otb 2015

Métriques

AUC

Résultats

Résultats de performance de divers modèles sur ce benchmark

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

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Visual Object Tracking On Otb 2015 | SOTA | HyperAI