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 176–200 of 525 papers

TitleStatusHype
A Complementary Tracking Model with Multiple Features—0
Learning Reinforced Attentional Representation for End-to-End Visual Tracking—0
Hierarchical Convolutional Features for Visual Tracking—0
Graph Attention Tracking—0
Hierarchical Instruction-aware Embodied Visual Tracking—0
Higher Performance Visual Tracking with Dual-Modal Localization—0
High-Performance Discriminative Tracking With Transformers—0
Learning Cascaded Siamese Networks for High Performance Visual Tracking—0
High Performance Visual Tracking with Circular and Structural Operators—0
Cross-Attention Guided Network for Visual Tracking—0
High precision control and deep learning-based corn stand counting algorithms for agricultural robot—0
Fast Kernelized Correlation Filters without Boundary Effect—0
How Effectively Can Indoor Wireless Positioning Relieve Visual Tracking Pains: A Camera-Rao Bound Viewpoint—0
Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion—0
HHTrack: Hyperspectral Object Tracking Using Hybrid Attention—0
Learning Compact Target-Oriented Feature Representations for Visual Tracking—0
Improving Accuracy and Generalization for Efficient Visual Tracking—0
Geometric Hypergraph Learning for Visual Tracking—0
Generic Multiview Visual Tracking—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking—0
Generalized mean shift with triangular kernel profile—0
Inducing Stronger Object Representations in Deep Visual Trackers—0
Fusion with Diffusion for Robust Visual Tracking—0
CREST: Convolutional Residual Learning for 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