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 1–50 of 341 papers

TitleStatusHype
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather ConditionsCode1
Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object TrackingCode1
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement LearningCode2
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
SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual TrackingCode1
UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory NetworkCode1
MITracker: Multi-View Integration for Visual Object Tracking—0
Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition BenchmarkCode2
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
Exploring Enhanced Contextual Information for Video-Level Object TrackingCode2
Visual Object Tracking across Diverse Data Modalities: A Review—0
A Distractor-Aware Memory for Visual Object Tracking with SAM2Code3
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware MemoryCode9
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model—0
NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object TrackingCode1
SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory TreeCode4
Improving Visual Object Tracking through Visual PromptingCode1
General Compression Framework for Efficient Transformer Object Tracking—0
Enhancing Nighttime UAV Tracking with Light Distribution SuppressionCode1
Progressive Representation Learning for Real-Time UAV TrackingCode2
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2Code5
Low-Light Object Tracking: A BenchmarkCode1
MambaEVT: Event Stream based Visual Object Tracking using State Space ModelCode1
SAM 2: Segment Anything in Images and VideosCode11
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
LoReTrack: Efficient and Accurate Low-Resolution Transformer TrackingCode1
Awesome Multi-modal Object TrackingCode5
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
RTracker: Recoverable Tracking via PN Tree Structured MemoryCode1
Exploring Dynamic Transformer for Efficient Object Tracking—0
OmniVid: A Generative Framework for Universal Video UnderstandingCode2
Elysium: Exploring Object-level Perception in Videos via MLLMCode2
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object TrackingCode2
Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers—0
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning—0
Tracking Meets LoRA: Faster Training, Larger Model, Stronger PerformanceCode2
VastTrack: Vast Category Visual Object TrackingCode2
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
ACTrack: Adding Spatio-Temporal Condition for Visual Object 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
6ARTrackV2-LAccuracy86.1—Unverified
7MixViT-L(ConvMAE)Accuracy86.1—Unverified
8SPMTrack-BAccuracy86.1—Unverified
9ODTrack-LAccuracy86.1—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
2LoRAT-L-378AUC0.73—Unverified
3NeighborTrack-OSTrackAUC0.73—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