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
Robust Visual Tracking via Convolutional Networks—0
Adaptive Objectness for Object Tracking—0
Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation—0
Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform—0
Visual Tracking Using Pertinent Patch Selection and Masking—0
Interval Tracker: Tracking by Interval Analysis—0
Partial Occlusion Handling for Visual Tracking via Robust Part Matching—0
Adaptive Color Attributes for Real-Time Visual Tracking—0
Visual Tracking via Probability Continuous Outlier Model—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
Minimum Uncertainty Gap for Robust Visual Tracking—0
Visual Tracking via Locality Sensitive Histograms—0
Part-Based Visual Tracking with Online Latent Structural Learning—0
Learning Compact Binary Codes for Visual Tracking—0
A Diffusion Process on Riemannian Manifold for Visual Tracking—0
StructBoost: Boosting Methods for Predicting Structured Output Variables—0
On the connections between saliency and tracking—0
Fusion with Diffusion for Robust Visual 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