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 251–300 of 341 papers

TitleStatusHype
Single Object Tracking Research: A Survey—0
Spectral Filter Tracking—0
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking—0
SRRT: Exploring Search Region Regulation for Visual Object Tracking—0
Real-time Visual Object Tracking with Natural Language Description—0
Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers—0
Towards a Better Match in Siamese Network Based Visual Object Tracker—0
Towards Efficient Training with Negative Samples in Visual Tracking—0
Towards real-time and energy efficient Siamese tracking -- a hardware-software approach—0
Tracking by 3D Model Estimation of Unknown Objects in Videos—0
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches—0
Tracking the Untrackable—0
Transforming Model Prediction for Tracking—0
Two stages for visual object tracking—0
Video Propagation Networks—0
Video Tracking Using Learned Hierarchical Features—0
Visual Object Tracking across Diverse Data Modalities: A Review—0
Visual Object Tracking based on Adaptive Siamese and Motion Estimation Network—0
Visual Object Tracking by Segmentation with Graph Convolutional Network—0
Visual Object Tracking in First Person Vision—0
Visual Object Tracking on Multi-modal RGB-D Videos: A Review—0
Visual object tracking performance measures revisited—0
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook—0
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation—0
X Modality Assisting RGBT Object Tracking—0
SiamVGG: Visual Tracking using Deeper Siamese NetworksCode0
Context-aware Deep Feature Compression for High-speed Visual TrackingCode0
SPARK: Spatial-aware Online Incremental Attack Against Visual TrackingCode0
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object TrackingCode0
Spatiotemporal CNN for Video Object SegmentationCode0
GradNet: Gradient-Guided Network for Visual Object TrackingCode0
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV TrackingCode0
3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving ObjectCode0
Good Features to Correlate for Visual TrackingCode0
CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule RoutingCode0
Fast Visual Object Tracking with Rotated Bounding BoxesCode0
Fast Video Object Segmentation by Reference-Guided Mask PropagationCode0
Staple: Complementary Learners for Real-Time TrackingCode0
Fast and Accurate Online Video Object Segmentation via Tracking PartsCode0
AViTMP: A Tracking-Specific Transformer for Single-Branch Visual TrackingCode0
Event-based Visual Tracking in Dynamic EnvironmentsCode0
EgoTracks: A Long-term Egocentric Visual Object Tracking DatasetCode0
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained VideosCode0
Visual Object Tracking: The Initialisation ProblemCode0
Efficient Visual Tracking with Exemplar TransformersCode0
Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus VerificationCode0
TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object TrackingCode0
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingCode0
Attentional Correlation Filter Network for Adaptive Visual TrackingCode0
Efficient Video Object Segmentation via Network ModulationCode0
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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