SOTAVerified

Visual Tracking

Visual Tracking is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.

Source: Learning Reinforced Attentional Representation for End-to-End Visual Tracking

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 525 papers

TitleStatusHype
Direct Visual-Inertial Odometry with Semi-Dense Mapping—0
Energy-Based Models for Deep Probabilistic RegressionCode1
Inducing Stronger Object Representations in Deep Visual Trackers—0
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator—0
GradNet: Gradient-Guided Network for Visual Object TrackingCode0
Human Following for Wheeled Robot with Monocular Pan-tilt Camera—0
Saliency based Semi-supervised Learning for Orbiting Satellite Tracking—0
Discriminative and Robust Online Learning for Siamese Visual TrackingCode0
Learning Reinforced Attentional Representation for End-to-End Visual Tracking—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning—0
Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus VerificationCode0
Learning Compact Target-Oriented Feature Representations for Visual Tracking—0
Learning the Model Update for Siamese TrackersCode0
Real Time Visual Tracking using Spatial-Aware Temporal Aggregation NetworkCode0
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking—0
Distilled Siamese Networks for Visual Tracking—0
Real-Time Correlation Tracking via Joint Model Compression and TransferCode0
Tracking Holistic Object RepresentationsCode1
Multi-Adapter RGBT Tracking—0
Visual Tracking via Dynamic Memory Networks—0
Learning Feature Embeddings for Discriminant Model based Tracking—0
Hallucinated Adversarial Learning for Robust Visual Tracking—0
Graph Convolutional TrackingCode0
Visual Tracking via Adaptive Spatially-Regularized Correlation FiltersCode0
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All datasetsTNL2KDAVISKineticsKubricRGB-StackingLaSOTOTB-100OTB-2013Second dialogue state tracking challengeTrackingNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ARTrack-LAUC60.3—Unverified
2UNINEXT-HAUC59.3—Unverified
3JointNLTAUC56.9—Unverified
4OSTrackAUC55.9—Unverified
5TransTAUC50.7—Unverified
6AdaSwitcherAUC42—Unverified
#ModelMetricClaimedVerifiedStatus
1TAPIR (Panning MOVi-E)Average Jaccard61.3—Unverified
2TAPIR (MOVi-E)Average Jaccard59.8—Unverified
#ModelMetricClaimedVerifiedStatus
1TAPIR (Panning MOVi-E)Average Jaccard57.2—Unverified
2TAPIR (MOVi-E)Average Jaccard57.1—Unverified
#ModelMetricClaimedVerifiedStatus
1TAPIR (Panning MOVi-E)Average Jaccard84.7—Unverified
2TAPIR (MOVi-E)Average Jaccard84.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAPIR (MOVi-E)Average Jaccard66.2—Unverified
2TAPIR (Panning MOVi-E)Average Jaccard62.7—Unverified
#ModelMetricClaimedVerifiedStatus
1TATrack-LAUC71.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SiamFC-lu (Ours)AUC0.32—Unverified
#ModelMetricClaimedVerifiedStatus
1SiamFC-lu (Ours)AUC0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1MDNetScore0.64—Unverified
#ModelMetricClaimedVerifiedStatus
1TATrack-LACCURACY0.85—Unverified