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Visual Object Tracking On Otb 2015

評価指標

AUC

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
AUC
Paper TitleRepository
SA-Siam0.657A Twofold Siamese Network for Real-Time Object Tracking-
KeepTrack0.709Learning Target Candidate Association to Keep Track of What Not to Track-
SeqTrack-L3840.683Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking-
SiamVGG0.654SiamVGG: Visual Tracking using Deeper Siamese Networks-
ODTrack-B0.723ODTrack: Online Dense Temporal Token Learning for Visual Tracking-
HIPTrack0.71HIPTrack: Visual Tracking with Historical Prompts-
GradNet-GradNet: Gradient-Guided Network for Visual Object Tracking-
TRASFUST0.701Tracking-by-Trackers with a Distilled and Reinforced Model-
ODTrack-L0.724ODTrack: Online Dense Temporal Token Learning for Visual Tracking-
ASRCF0.692Visual Tracking via Adaptive Spatially-Regularized Correlation Filters
CFNet0.568End-to-end representation learning for Correlation Filter based tracking-
SAMURAI-L0.715SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory-
STMTrack0.719STMTrack: Template-free Visual Tracking with Space-time Memory Networks-
UniTrack_DCF0.618Do Different Tracking Tasks Require Different Appearance Models?-
SE-SiamFC0.66Scale Equivariance Improves Siamese Tracking-
AAA0.70AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee-
PiVOT-L0.712Improving Visual Object Tracking through Visual Prompting-
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Visual Object Tracking On Otb 2015 | SOTA | HyperAI超神経