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 101–150 of 341 papers

TitleStatusHype
AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance GuaranteeCode1
Self-supervised Object Tracking with Cycle-consistent Siamese NetworksCode1
Scale Equivariance Improves Siamese TrackingCode1
Tracking-by-Trackers with a Distilled and Reinforced ModelCode1
Ocean: Object-aware Anchor-free TrackingCode1
Siamese Keypoint Prediction Network for Visual Object TrackingCode1
TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generatorCode1
How to Train Your Energy-Based Model for RegressionCode1
Deformable Siamese Attention Networks for Visual Object TrackingCode1
High-Performance Long-Term Tracking with Meta-UpdaterCode1
PAC Confidence Sets for Deep Neural Networks via Calibrated PredictionCode1
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation GuidelinesCode1
Energy-Based Models for Deep Probabilistic RegressionCode1
Tracking Holistic Object RepresentationsCode1
Deeper and Wider Siamese Networks for Real-Time Visual TrackingCode1
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep NetworksCode1
YouTube-VOS: Sequence-to-Sequence Video Object SegmentationCode1
Deep Convolutional Neural Networks for Thermal Infrared Object TrackingCode1
One-Shot Video Object SegmentationCode1
Fully Spiking Neural Networks for Unified Frame-Event Object Tracking—0
Progressive Scaling Visual Object Tracking—0
Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking—0
Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast ApproachCode0
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV TrackingCode0
Adversarial Attack for RGB-Event based Visual Object TrackingCode0
MITracker: Multi-View Integration for Visual Object Tracking—0
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—0
DreamTrack: Dreaming the Future for Multimodal Visual Object Tracking—0
Autoregressive Sequential Pretraining for Visual Tracking—0
Visual Object Tracking across Diverse Data Modalities: A Review—0
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model—0
General Compression Framework for Efficient Transformer Object Tracking—0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
Robust compressive tracking via online weighted multiple instance learning—0
Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based FusionCode0
TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object TrackingCode0
360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos—0
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking AttacksCode0
Exploring Dynamic Transformer for Efficient Object Tracking—0
Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers—0
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning—0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
ACTrack: Adding Spatio-Temporal Condition for Visual Object Tracking—0
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking—0
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
X Modality Assisting RGBT Object Tracking—0
Tracking Skiers from the Top to the BottomCode0
AViTMP: A Tracking-Specific Transformer for Single-Branch Visual TrackingCode0
Distractor-aware Event-based Tracking—0
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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