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 301–325 of 525 papers

TitleStatusHype
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking—0
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention—0
Online Decision Based Visual Tracking via Reinforcement Learning—0
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking—0
Online Metric-Weighted Linear Representations for Robust Visual Tracking—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network—0
Online Unsupervised Feature Learning for Visual Tracking—0
Online Visual Tracking with One-Shot Context-Aware Domain Adaptation—0
On the connections between saliency and tracking—0
Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking—0
Learning Cascaded Siamese Networks for High Performance Visual Tracking—0
Part-Based Visual Tracking with Online Latent Structural Learning—0
Partial Occlusion Handling for Visual Tracking via Robust Part Matching—0
Particle filter re-detection for visual tracking via correlation filters—0
Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking—0
Patchwise object tracking via structural local sparse appearance model—0
Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model—0
PG-Net: Pixel to Global Matching Network for Visual Tracking—0
Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle—0
Prediction-Tracking-Segmentation—0
CREST: Convolutional Residual Learning for Visual Tracking—0
Probabilistic Regression for Visual Tracking—0
Progressive Multi-Stage Learning for Discriminative Tracking—0
Progressive Unsupervised Learning for Visual Object 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