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 451–500 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
Recurrently Target-Attending Tracking—0
In Defense of Sparse Tracking: Circulant Sparse Tracker—0
STCT: Sequentially Training Convolutional Networks for Visual Tracking—0
Hedged Deep Tracking—0
Structural Correlation Filter for Robust Visual Tracking—0
ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements—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
Local Subspace Collaborative Tracking—0
Linearization to Nonlinear Learning for Visual Tracking—0
SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking—0
Hierarchical Convolutional Features for Visual Tracking—0
Multi-Kernel Correlation Filter for Visual Tracking—0
Multiple Feature Fusion via Weighted Entropy for Visual Tracking—0
Visual Tracking With Fully Convolutional Networks—0
First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks—0
Learning Multi-Domain Convolutional Neural Networks for Visual TrackingCode0
Relaxed Multiple-Instance SVM with Application to Object Discovery—0
Visual Tracking via Nonnegative Regularization Multiple Locality Coding—0
Efficient Discriminative Nonorthogonal Binary Subspace with its Application to Visual Tracking—0
Robust Visual Tracking via Inverse Nonnegative Matrix Factorization—0
Online Metric-Weighted Linear Representations for Robust Visual Tracking—0
Long-Term Correlation Tracking—0
Real-Time Part-Based Visual Tracking via Adaptive Correlation Filters—0
Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches—0
Towards Force Sensing From Vision: Observing Hand-Object Interactions to Infer Manipulation Forces—0
Multihypothesis Trajectory Analysis for Robust Visual Tracking—0
Structural Sparse Tracking—0
Unsupervised Learning of Visual Representations using Videos—0
Understanding and Diagnosing Visual Tracking Systems—0
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking—0
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network—0
Visual object tracking performance measures revisited—0
Transferring Rich Feature Hierarchies for Robust Visual Tracking—0
Instance Significance Guided Multiple Instance Boosting for Robust 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