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Object Tracking On Coesot

Metrics

Precision Rate

Success Rate

Results

Performance results of various models on this benchmark

Model Name	Precision Rate	Success Rate	Paper Title	Repository
AiATrack	67.4	59.0	AiATrack: Attention in Attention for Transformer Visual Tracking	-
KeepTrack	66.1	59.6	Learning Target Candidate Association to Keep Track of What Not to Track	-
HR-CEUTrack-Base	71.9	63.2	Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers	-
SuperDiMP	67.0	60.2	-	-
HR-CEUTrack-Large	73.8	65.0	Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers	-
KYS	66.7	58.6	Know Your Surroundings: Exploiting Scene Information for Object Tracking	-
CEUTrack-Base	70.5	62.0	Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric	-
SiamR-CNN	67.5	60.9	Siam R-CNN: Visual Tracking by Re-Detection	-
TransT	67.9	60.5	Transformer Tracking	-
CEUTrack-Large	71.4	62.8	Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric	-
OSTrack	66.6	59.0	Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework	-
TrDiMP	66.9	60.1	Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking	-

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Object Tracking On Coesot | SOTA | HyperAI