Visual Object Tracking On Got 10K

평가 지표

Average Overlap

Success Rate 0.5

Success Rate 0.75

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Average Overlap	Success Rate 0.5	Success Rate 0.75	Paper Title	Repository
SLT-TransT	67.5	76.8	60.3	Towards Sequence-Level Training for Visual Tracking
OSTrack-384	73.7	83.2	70.8	Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
MCITrack-B224	77.9	88.2	76.8	Exploring Enhanced Contextual Information for Video-Level Object Tracking
MCITrack-L384	80.0	88.5	80.2	Exploring Enhanced Contextual Information for Video-Level Object Tracking
DiMP	61.1	71.7	-	Learning Discriminative Model Prediction for Tracking
SeqTrack-L384	74.8	81.9	72.2	Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
Siam R-CNN	64.9	72.8	-	Siam R-CNN: Visual Tracking by Re-Detection	-
MITS	80.4	89.8	75.8	Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
STMTrack	64.2	73.7	57.5	STMTrack: Template-free Visual Tracking with Space-time Memory Networks
ATOM	61.0	74.2	-	ATOM: Accurate Tracking by Overlap Maximization
ARTrackV2-L	79.5	87.8	79.6	ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
TRASFUST	61.7	72.9	-	Tracking-by-Trackers with a Distilled and Reinforced Model
NeighborTrack-OSTrack	75.7	85.72	73.3	NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
ARTrack-L	78.5	87.4	77.8	Autoregressive Visual Tracking
FEAR-M	62.3	-	-	FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
SAMURAI-L	81.7	92.2	76.9	SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory	-
TATrack-L-GOT	76.6	85.7	73.4	Target-Aware Tracking with Long-term Context Attention	-
Ocean	61.1	72.1	-	Ocean: Object-aware Anchor-free Tracking
MixFormer-1k	71.2	79.9	65.8	MixFormer: End-to-End Tracking with Iterative Mixed Attention
FEAR-L	64.5	-	-	FEAR: Fast, Efficient, Accurate and Robust Visual Tracker

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