SOTAVerified

Visual Object Tracking

Visual Object Tracking is an important research topic in computer vision, image understanding and pattern recognition. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent video frames.

Source: Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 341 papers

TitleStatusHype
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking—0
Spatio-temporal Prompting Network for Robust Video Feature ExtractionCode1
Correlation-Embedded Transformer Tracking: A Single-Branch FrameworkCode1
Unifying Visual and Vision-Language Tracking via Contrastive LearningCode1
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
Explicit Visual Prompts for Visual Object TrackingCode1
ODTrack: Online Dense Temporal Token Learning for Visual TrackingCode2
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to DescribeCode2
X Modality Assisting RGBT Object Tracking—0
Tracking Skiers from the Top to the BottomCode0
HIPTrack: Visual Tracking with Historical PromptsCode1
AViTMP: A Tracking-Specific Transformer for Single-Branch Visual TrackingCode0
Distractor-aware Event-based Tracking—0
Putting the Object Back into Video Object SegmentationCode3
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel BaselineCode2
RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration—0
BASE: Probably a Better Approach to Multi-Object Tracking—0
Leveraging the Power of Data Augmentation for Transformer-based Tracking—0
Mobile Vision Transformer-based Visual Object TrackingCode1
Separable Self and Mixed Attention Transformers for Efficient Object TrackingCode1
Efficient Training for Visual Tracking with Deformable Transformer—0
Towards Efficient Training with Negative Samples in Visual Tracking—0
Improving Underwater Visual Tracking With a Large Scale Dataset and Image EnhancementCode1
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and SegmentationCode1
BackTrack: Robust template update via Backward Tracking of candidate template—0
Learning Spatial Distribution of Long-Term Trackers Scores—0
360VOT: A New Benchmark Dataset for Omnidirectional Visual Object TrackingCode1
Tracking Anything in High QualityCode2
Cross-Drone Transformer Network for Robust Single Object TrackingCode1
Heteroskedastic Geospatial Tracking with Distributed Camera Networks—0
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object TrackingCode1
Tracking by 3D Model Estimation of Unknown Objects in Videos—0
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking TasksCode1
Generalized Relation Modeling for Transformer TrackingCode1
SiamTHN: Siamese Target Highlight Network for Visual Tracking—0
Universal Instance Perception as Object Discovery and RetrievalCode3
Target-Aware Tracking with Long-term Context AttentionCode1
MixFormer: End-to-End Tracking with Iterative Mixed AttentionCode2
Compact Transformer Tracker with Correlative Masked ModelingCode1
EgoTracks: A Long-term Egocentric Visual Object Tracking DatasetCode0
Foreground-Background Distribution Modeling Transformer for Visual Object Tracking—0
Autoregressive Visual TrackingCode2
Representation Learning for Visual Object Tracking by Masked Appearance TransferCode1
Event-based Visual Tracking in Dynamic EnvironmentsCode0
Improving Siamese Based Trackers with Light or No Training through Multiple Templates and Temporal Network—0
Roboflow 100: A Rich, Multi-Domain Object Detection BenchmarkCode2
PVT++: A Simple End-to-End Latency-Aware Visual Tracking FrameworkCode1
3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving ObjectCode0
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor TrackletsCode1
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All datasetsLaSOTGOT-10kTrackingNetLaSOT-extOTB-2015UAV123TNL2KVOT2017/18DiDiNeedForSpeedYouTube-VOS 2018AVisT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPMTrack-GAUC77.4—Unverified
2SPMTrack-LAUC76.8—Unverified
3MCITrack-L384AUC76.6—Unverified
4LoRAT-g-378AUC76.2—Unverified
5MCITrack-B224AUC75.3—Unverified
6DAM4SAMAUC75.1—Unverified
7LoRAT-L-378AUC75.1—Unverified
8SPMTrack-BAUC74.9—Unverified
9RTracker-LAUC74.7—Unverified
10SAMURAI-LAUC74.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SAMURAI-LAverage Overlap81.7—Unverified
2DAM4SAMAverage Overlap81.1—Unverified
3SPMTrack-GAverage Overlap81—Unverified
4MITSAverage Overlap80.4—Unverified
5MCITrack-L384Average Overlap80—Unverified
6SPMTrack-LAverage Overlap80—Unverified
7ARTrackV2-LAverage Overlap79.5—Unverified
8LoRAT-g-378Average Overlap78.9—Unverified
9ARTrack-LAverage Overlap78.5—Unverified
10ODTrack-LAverage Overlap78.2—Unverified
#ModelMetricClaimedVerifiedStatus
1DropTrackNormalized Precision88.9—Unverified
2MCITrack-L384Accuracy87.9—Unverified
3SPMTrack-GAccuracy87.3—Unverified
4SPMTrack-LAccuracy86.9—Unverified
5MCITrack-B224Accuracy86.3—Unverified
6ODTrack-LAccuracy86.1—Unverified
7ARTrackV2-LAccuracy86.1—Unverified
8SPMTrack-BAccuracy86.1—Unverified
9MixViT-L(ConvMAE)Accuracy86.1—Unverified
10LoRAT-g-378Accuracy86—Unverified
#ModelMetricClaimedVerifiedStatus
1SAMURAI-LAUC61—Unverified
2DAM4SAMAUC60.9—Unverified
3LoRAT-L-378AUC56.6—Unverified
4LoRAT-g-378AUC56.5—Unverified
5UNINEXT-HAUC56.2—Unverified
6MCITrack-L384AUC55.7—Unverified
7RTracker-LAUC54.9—Unverified
8MCITrack-B224AUC54.6—Unverified
9ODTrack-LAUC53.9—Unverified
10ARTrackV2-LAUC53.4—Unverified
#ModelMetricClaimedVerifiedStatus
1GradNetPrecision0.86—Unverified
2SPMTrack-BAUC0.73—Unverified
3ODTrack-LAUC0.72—Unverified
4ODTrack-BAUC0.72—Unverified
5STMTrackAUC0.72—Unverified
6SAMURAI-LAUC0.72—Unverified
7PiVOT-LAUC0.71—Unverified
8HIPTrackAUC0.71—Unverified
9KeepTrackAUC0.71—Unverified
10TRASFUSTAUC0.7—Unverified
#ModelMetricClaimedVerifiedStatus
1LoRAT-g-378AUC0.74—Unverified
2NeighborTrack-OSTrackAUC0.73—Unverified
3LoRAT-L-378AUC0.73—Unverified
4SPMTrack-BAUC0.72—Unverified
5ARTrackV2-LAUC0.72—Unverified
6ARTrack-LAUC0.71—Unverified
7OSTrack -384AUC0.71—Unverified
8AiATrackAUC0.71—Unverified
9HIPTrackAUC0.71—Unverified
10MixFormerAUC0.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MCITrack-L384AUC65.3—Unverified
2SPMTrack-GAUC64.7—Unverified
3SPMTrack-LAUC63.7—Unverified
4MCITrack-B224AUC62.9—Unverified
5LoRAT-g-378AUC62.7—Unverified
6LoRAT-L-378AUC62.3—Unverified
7SPMTrack-BAUC62—Unverified
8ODTrack-LAUC61.7—Unverified
9ARTrackV2-LAUC61.6—Unverified
10ODTrack-BAUC60.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SiamMask_EExpected Average Overlap (EAO)0.45—Unverified
2SiamFC++Expected Average Overlap (EAO)0.43—Unverified
3SiamRPN++_RExpected Average Overlap (EAO)0.42—Unverified
4THOR-SiamRPNExpected Average Overlap (EAO)0.42—Unverified
5SiamRPN++Expected Average Overlap (EAO)0.41—Unverified
6THOR-SiamMaskExpected Average Overlap (EAO)0.41—Unverified