SOTAVerified

Pose Tracking

Pose Tracking is the task of estimating multi-person human poses in videos and assigning unique instance IDs for each keypoint across frames. Accurate estimation of human keypoint-trajectories is useful for human action recognition, human interaction understanding, motion capture and animation.

Source: LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 191 papers

TitleStatusHype
A Top-down Approach to Articulated Human Pose Estimation and Tracking—0
Attainable Force Approximation and Full-Pose Tracking Control of an Over-Actuated Thrust-Vectoring Modular Team UAV—0
A Unified Multi-view Multi-person Tracking Framework—0
Automated Tracking of Primate Behavior—0
A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System for Rigid Objects—0
Breathless: An 8-hour Performance Contrasting Human and Robot Expressiveness—0
Bring Your Rear Cameras for Egocentric 3D Human Pose Estimation—0
Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions—0
Combining detection and tracking for human pose estimation in videos—0
Comparison of Visual Trackers for Biomechanical Analysis of Running—0
Counter-Hypothetical Particle Filters for Single Object Pose Tracking—0
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview—0
Design, Implementation and Evaluation of an External Pose-Tracking System for Underwater Cameras—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulation—0
Efficient Circle-Based Camera Pose Tracking Free of PnP—0
Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields—0
Efficient Pose Tracking from Natural Features in Standard Web Browsers—0
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect—0
Event-based Camera Tracker by t NeRF—0
EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping—0
Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose—0
Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies—0
FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks—0
Fluoroscopic Shape and Pose Tracking of Catheters with Custom Radiopaque Markers—0
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All datasetsPoseTrack2017PoseTrack2018Multi-Person PoseTrack

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DetTrackMOTA64.09—Unverified
2KeyTrackMOTA61.15—Unverified
3LightTrackMOTA58.01—Unverified
4HRNet-W48 COCOMOTA57.93—Unverified
5MSRA (FlowTrack)MOTA57.81—Unverified
6TML++ (MIPAL)MOTA54.46—Unverified
7STAFMOTA53.81—Unverified
8ProTrackerMOTA51.82—Unverified
9PoseFlowMOTA50.98—Unverified
10PoseTrackMOTA48.37—Unverified
#ModelMetricClaimedVerifiedStatus
1DetTrackMOTA64.3—Unverified
2UniTrackMOTA63.5—Unverified
34DHumans + ViTDetMOTA61.9—Unverified
4MSRAMOTA61.37—Unverified
5TML++ (MIPAL)MOTA54.86—Unverified
#ModelMetricClaimedVerifiedStatus
1PoseTrackMOTA28.2—Unverified