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
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties—0
An Enhanced MPPT Method based on ANN-assisted Sequential Monte Carlo and Quickest Change Detection—0
Adaptive Step Size Incremental Conductance Based Maximum Power Point Tracking (MPPT)—0
TAPTR: Tracking Any Point with Transformers as Detection—0
TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video—0
Analysis, design, and implementation of the AFZ converter applied to photovoltaic systems—0
Analysis and implementation of the Buck-Boost Modified Series Forward converter applied to photovoltaic systems—0
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion—0
Topology-Based Feature Design and Tracking for Multi-Center Cyclones—0
Toward Geometric Deep SLAM—0
Toward Scalable, Flexible Scene Flow for Point Clouds—0
EgoPoints: Advancing Point Tracking for Egocentric Videos—0
Endo-TTAP: Robust Endoscopic Tissue Tracking via Multi-Facet Guided Attention and Hybrid Flow-point Supervision—0
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm—0
Edge SLAM: Edge Points Based Monocular Visual SLAM—0
DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction—0
Extremum Power Seeking Control of A Hybrid Wind-Solar-Storage DC Power System—0
Fast Maximum Power Point Tracking for PV Arrays under Partial shaded Conditions—0
Dynamic Camera Poses and Where to Find Them—0
Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation—0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning—0
DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos—0
Geometric PID Controller for Stabilization of Nonholonomic Mechanical Systems on Lie Groups—0
GIFT: Generated Indoor video frames for Texture-less point tracking—0
GS-DiT: Advancing Video Generation with Dynamic 3D Gaussian Fields through Efficient Dense 3D Point Tracking—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