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 76–100 of 151 papers

TitleStatusHype
Transformer based time series prediction of the maximum power point for solar photovoltaic cells—0
Foundation Models for Amodal Video Instance Segmentation in Automated DrivingCode0
Tracking Any Point with Frame-Event Fusion Network at High Frame RateCode0
Leveraging Object Priors for Point TrackingCode0
DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction—0
Can Visual Foundation Models Achieve Long-term Point Tracking?—0
Kalib: Easy Hand-Eye Calibration with Reference Point Tracking—0
A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-ShotCode0
Autogenic Language Embedding for Coherent Point TrackingCode0
PlantTrack: Task-Driven Plant Keypoint Tracking with Zero-Shot Sim2Real Transfer—0
Long-Term 3D Point Tracking By Cost Volume Fusion—0
Power System Architecture and Control for Green Hydrogen Production via Power Converter-less Photovoltaic-Electrolyser Integration—0
Optimal Video Compression using Pixel Shift TrackingCode0
Training-Free Robust Interactive Video Object Segmentation—0
Fast Encoder-Based 3D from Casual Videos via Point Track Processing—0
Multiple model estimation under perspective of random-fuzzy dual interpretation of unknown uncertaintyCode0
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023—0
DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video—0
TAPTR: Tracking Any Point with Transformers as Detection—0
Hierarchical Provision of Distribution Grid Flexibility with Online Feedback Optimization—0
PIPsUS: Self-Supervised Dense Point Tracking in UltrasoundCode0
StableDrag: Stable Dragging for Point-based Image Editing—0
Tracking-in-range Formulations for Numerical Optimal Control—0
DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point TrackersCode0
Analysis, design, and implementation of the AFZ converter applied to photovoltaic systems—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