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
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Require Process Control? LSTMc is all you need!—0
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation—0
Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations—0
Neural Eulerian Scene Flow Fields—0
Set-Point Tracking MPC with Avoidance Features—0
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023—0
Spatiotemporal Feature Learning for Event-Based Vision—0
Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) ChallengeCode0
A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-ShotCode0
PIPsUS: Self-Supervised Dense Point Tracking in UltrasoundCode0
Linear Battery Models for Power Systems AnalysisCode0
Leveraging Object Priors for Point TrackingCode0
Visual Material Characteristics Learning for Circular HealthcareCode0
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingCode0
Tracking Any Point with Frame-Event Fusion Network at High Frame RateCode0
Optimal Video Compression using Pixel Shift TrackingCode0
Online Gradient Descent for Flexible Power Point Tracking Under a Highly Fluctuating Weather and LoadCode0
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detectionCode0
Foundation Models for Amodal Video Instance Segmentation in Automated DrivingCode0
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement LearningCode0
Skin feature point tracking using deep feature encodingsCode0
DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point TrackersCode0
Multiple model estimation under perspective of random-fuzzy dual interpretation of unknown uncertaintyCode0
Autogenic Language Embedding for Coherent Point TrackingCode0
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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