HyperAI超神经
首页资讯最新论文教程数据集百科SOTALLM 模型天梯GPU 天梯顶会
全站搜索
关于
HyperAI超神经

Visual Object Tracking On Otb 2015

评估指标

AUC

评测结果

各个模型在此基准测试上的表现结果

模型名称
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
0 of 17 row(s) selected.