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

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Showing 51–100 of 341 papers

TitleStatusHype
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and SegmentationCode1
360VOT: A New Benchmark Dataset for Omnidirectional Visual Object TrackingCode1
Cross-Drone Transformer Network for Robust Single Object TrackingCode1
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object TrackingCode1
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking TasksCode1
Generalized Relation Modeling for Transformer TrackingCode1
Target-Aware Tracking with Long-term Context AttentionCode1
Compact Transformer Tracker with Correlative Masked ModelingCode1
Representation Learning for Visual Object Tracking by Masked Appearance TransferCode1
PVT++: A Simple End-to-End Latency-Aware Visual Tracking FrameworkCode1
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor TrackletsCode1
ProContEXT: Exploring Progressive Context Transformer for TrackingCode1
Trans2k: Unlocking the Power of Deep Models for Transparent Object TrackingCode1
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object TrackingCode1
Learning Spatial-Frequency Transformer for Visual Object TrackingCode1
Towards Sequence-Level Training for Visual TrackingCode1
AiATrack: Attention in Attention for Transformer Visual TrackingCode1
Towards Robust Video Object Segmentation with Adaptive Object CalibrationCode1
Revealing the Dark Secrets of Masked Image ModelingCode1
Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive AnalysisCode1
Transformer Tracking with Cyclic Shifting Window AttentionCode1
Unsupervised Learning of Accurate Siamese TrackingCode1
Robust Visual Tracking by SegmentationCode1
Backbone is All Your Need: A Simplified Architecture for Visual Object TrackingCode1
Correlation-Aware Deep TrackingCode1
Global Instance Tracking: Locating Target More Like HumansCode1
Few-Shot Backdoor Attacks on Visual Object TrackingCode1
Temporal Aggregation for Adaptive RGBT TrackingCode1
Exploring Fusion Strategies for Accurate RGBT Visual Object TrackingCode1
FEAR: Fast, Efficient, Accurate and Robust Visual TrackerCode1
SwinTrack: A Simple and Strong Baseline for Transformer TrackingCode1
Learning to Adversarially Blur Visual Object TrackingCode1
Do Different Tracking Tasks Require Different Appearance Models?Code1
Associating Objects with Transformers for Video Object SegmentationCode1
Target Transformed Regression for Accurate TrackingCode1
STMTrack: Template-free Visual Tracking with Space-time Memory NetworksCode1
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity PerspectiveCode1
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and BenchmarkCode1
Learning Spatio-Temporal Transformer for Visual TrackingCode1
Transformer TrackingCode1
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object TrackingCode1
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual TrackingCode1
PatchNet -- Short-range Template Matching for Efficient Video ProcessingCode1
All-Day Object Tracking for Unmanned Aerial VehicleCode1
SSTVOS: Sparse Spatiotemporal Transformers for Video Object SegmentationCode1
Rotation Equivariant Siamese Networks for TrackingCode1
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box EstimationCode1
Multi-modal Visual Tracking: Review and Experimental ComparisonCode1
Learning to Fuse Asymmetric Feature Maps in Siamese TrackersCode1
Learning Spatio-Appearance Memory Network for High-Performance Visual TrackingCode1
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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