Visual Object Tracking On Trackingnet

评估指标

Accuracy

Normalized Precision

Precision

评测结果

各个模型在此基准测试上的表现结果

模型名称	Accuracy	Normalized Precision	Precision	Paper Title	Repository
ARTrack-L	85.6	89.6	86.0	Autoregressive Visual Tracking
SiamFC++	74.5	79.8	68.5	SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
ATOM(Resnet18)+EnergyRegression	-	80.1	69.7	Energy-Based Models for Deep Probabilistic Regression
NeighborTrack-OSTrack	83.79	88.30	-	NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
GFS-DCF	60.9	71.79	56.57	Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
SiamBAN-ACM	75.3	81.0	71.2	Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
ODTrack-B	85.1	-	-	ODTrack: Online Dense Temporal Token Learning for Visual Tracking
DiMP-50	74.0	80.1	-	Learning Discriminative Model Prediction for Tracking
UNINEXT-H	85.4	89.0	86.4	Universal Instance Perception as Object Discovery and Retrieval
OSTrack-384	83.9	88.5	83.2	Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
MixFormer-L	83.9	88.9	83.1	MixFormer: End-to-End Tracking with Iterative Mixed Attention
MixFormerV2-B	83.4	88.1	81.6	-	-
TREG	78.5	83.8	75	Target Transformed Regression for Accurate Tracking
SeqTrack-L384	85.5	89.8	85.8	Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
ATOM	70.34	77.11	64.84	ATOM: Accurate Tracking by Overlap Maximization
AiATrack	82.7	87.8	80.4	AiATrack: Attention in Attention for Transformer Visual Tracking
LoRAT-g-378	86.0	90.2	86.1	Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
SLT-TransT	82.8	87.5	81.4	Towards Sequence-Level Training for Visual Tracking
MCITrack-B224	86.3	90.9	86.1	Exploring Enhanced Contextual Information for Video-Level Object Tracking
TATrack-L	85.0	89.3	84.5	Target-Aware Tracking with Long-term Context Attention	-

0 of 37 row(s) selected.