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 501–525 of 525 papers

TitleStatusHype
Transferring Rich Feature Hierarchies for Robust Visual Tracking—0
Adaptive Objectness for Object Tracking—0
Partial Occlusion Handling for Visual Tracking via Robust Part Matching—0
Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform—0
Adaptive Color Attributes for Real-Time Visual Tracking—0
Interval Tracker: Tracking by Interval Analysis—0
Visual Tracking Using Pertinent Patch Selection and Masking—0
Visual Tracking via Probability Continuous Outlier Model—0
Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation—0
Object Tracking via Non-Euclidean Geometry: A Grassmann Approach—0
Visual Tracking using Particle Swarm Optimization—0
Learning a Deep Compact Image Representation for Visual Tracking—0
Fast Tracking via Spatio-Temporal Context Learning—0
Online Unsupervised Feature Learning for Visual Tracking—0
Learning Compact Binary Codes for Visual Tracking—0
Minimum Uncertainty Gap for Robust Visual Tracking—0
Part-Based Visual Tracking with Online Latent Structural Learning—0
Visual Tracking via Locality Sensitive Histograms—0
A Diffusion Process on Riemannian Manifold for Visual Tracking—0
StructBoost: Boosting Methods for Predicting Structured Output Variables—0
Fusion with Diffusion for Robust Visual Tracking—0
On the connections between saliency and tracking—0
Real-time face swapping as a tool for understanding infant self-recognition—0
Multiple Instance Filtering—0
Kalman Filters and Homography: Utilizing the Matrix A—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