SOTAVerified

Point Tracking

Point Tracking, often referred to as Tracking any Point (TAP) involves acquiring, focusing on, and continuously tracking specific target point/points across video frames. The system identifies the target point, maintains focus, and predicts its movement, enabling smooth tracking even if the target moves unpredictably, or through occlusions. TAP has wide applications like object tracking, surveillance, and autonomous navigation.

Papers

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Showing 126–150 of 151 papers

TitleStatusHype
Data-driven predictive control with improved performance using segmented trajectories—0
New Hybrid Maximum Power Point Tracking Methods for Fuel Cell using Artificial Intelligent—0
A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems with Power Reserves and Fast Frequency Response Capabilities—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Adaptive Step Size Incremental Conductance Based Maximum Power Point Tracking (MPPT)—0
Topology-Based Feature Design and Tracking for Multi-Center Cyclones—0
Extremum Power Seeking Control of A Hybrid Wind-Solar-Storage DC Power System—0
A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery—0
MMC-Based Distributed Maximum Power Point Tracking for Photovoltaic Systems—0
Autonomous Control of a Quadrotor-Manipulator; Application of Extended State Disturbance Observer—0
Spatiotemporal Feature Learning for Event-Based Vision—0
Edge SLAM: Edge Points Based Monocular Visual SLAM—0
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties—0
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation—0
Partial Shading Detection and Smooth Maximum Power Point Tracking of PV Arrays under PSC—0
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement LearningCode0
Fast Maximum Power Point Tracking for PV Arrays under Partial shaded Conditions—0
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of VehiclesCode0
An Enhanced MPPT Method based on ANN-assisted Sequential Monte Carlo and Quickest Change Detection—0
Toward Geometric Deep SLAM—0
CoMaL Tracking: Tracking Points at the Object Boundaries—0
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingCode0
A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection—0
Trajectory Aligned Features For First Person Action Recognition—0
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression—0
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All datasetsTAP-Vid-DAVISPointOdysseyTAP-Vid-DAVIS-FirstTAP-Vid-KineticsTAP-Vid-Kinetics-FirstTAP-Vid-RGB-StackingPerception TestTAP-Vid

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LocoTrack-BAverage Jaccard69.4—Unverified
2BootsTAPIRAverage Jaccard66.2—Unverified
3CoTrackerAverage Jaccard65.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PIPs++Survival50.47—Unverified
2PIPs+Survival49.88—Unverified
#ModelMetricClaimedVerifiedStatus
1LocoTrack-BAverage Jaccard64.8—Unverified
2CoTrackerAverage Jaccard62.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BootsTAPIRAverage Jaccard61.4—Unverified
2LocoTrack-BAverage Jaccard59.1—Unverified
#ModelMetricClaimedVerifiedStatus
1LocoTrack-BAverage Jaccard52.3—Unverified
2CoTrackerAverage Jaccard48.8—Unverified
#ModelMetricClaimedVerifiedStatus
1BootsTAPIRAverage Jaccard72.4—Unverified
2LocoTrack-BAverage Jaccard70.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Static BaselineAverage Jaccard0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1PIPs++MTE4.6—Unverified