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 201–225 of 525 papers

TitleStatusHype
Self-Supervised Tracking via Target-Aware Data Synthesis—0
Generalized mean shift with triangular kernel profile—0
Fusion with Diffusion for Robust Visual Tracking—0
CREST: Convolutional Residual Learning for Visual Tracking—0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking—0
Is First Person Vision Challenging for Object Tracking?—0
Is First Person Vision Challenging for Object Tracking?—0
Adaptive Objectness for Object Tracking—0
Learning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential—0
Deep Motion Features for Visual Tracking—0
Accurate Bounding-box Regression with Distance-IoU Loss for Visual Tracking—0
BASE: Probably a Better Approach to Multi-Object Tracking—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking—0
Learning Target Candidate Association to Keep Track of What Not to Track—0
FPGA-based Acceleration System for Visual Tracking—0
Learning a Deep Compact Image Representation for Visual Tracking—0
Deep Tracking: Visual Tracking Using Deep Convolutional Networks—0
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking—0
Learning Compact Binary Codes for Visual Tracking—0
Learning Compact Target-Oriented Feature Representations for Visual Tracking—0
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking—0
Counterfactual Reasoning about Intent for Interactive Navigation in Dynamic Environments—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks—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