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

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Showing 451–475 of 525 papers

TitleStatusHype
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?—0
Visual Tracking via Shallow and Deep Collaborative Model—0
Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent NetworkCode0
Action Recognition with Joint Attention on Multi-Level Deep Features—0
Hedged Deep Tracking—0
Structural Correlation Filter for Robust Visual Tracking—0
STCT: Sequentially Training Convolutional Networks for Visual Tracking—0
ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements—0
Recurrently Target-Attending Tracking—0
In Defense of Sparse Tracking: Circulant Sparse Tracker—0
Visual Tracking Using Attention-Modulated Disintegration and Integration—0
Sparse Coding and Counting for Robust Visual Tracking—0
Deformable Parts Correlation Filters for Robust Visual Tracking—0
Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking—0
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking—0
Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle—0
Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion—0
Geometric Hypergraph Learning for Visual Tracking—0
A Universal Update-pacing Framework For Visual TrackingCode0
Visual Tracking via Reliable Memories—0
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random MeasurementsCode0
Learning Support Correlation Filters for Visual Tracking—0
Deep Tracking: Visual Tracking Using Deep Convolutional Networks—0
Hierarchical Convolutional Features for Visual Tracking—0
Multiple Feature Fusion via Weighted Entropy for Visual Tracking—0
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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