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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 141–150 of 151 papers

Title	Date	Tasks	Status
Fast Maximum Power Point Tracking for PV Arrays under Partial shaded Conditions	Sep 17, 2018	Point Tracking	—Unverified
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement Learning	Sep 17, 2018	Deep Reinforcement LearningPoint Tracking	CodeCode Available
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles	Jun 1, 2018	3D ReconstructionPoint Tracking	CodeCode Available
An Enhanced MPPT Method based on ANN-assisted Sequential Monte Carlo and Quickest Change Detection	May 13, 2018	Change DetectionPoint Tracking	—Unverified
Toward Geometric Deep SLAM	Jul 24, 2017	CPUPoint Tracking	—Unverified
CoMaL Tracking: Tracking Points at the Object Boundaries	Jun 7, 2017	ObjectPoint Tracking	—Unverified
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking	Aug 12, 2016	Object TrackingPoint Tracking	CodeCode Available
A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection	May 26, 2016	Dynamic Time WarpingGesture Recognition	—Unverified
Trajectory Aligned Features For First Person Action Recognition	Apr 7, 2016	Action RecognitionPoint Tracking	—Unverified
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression	Dec 27, 2015	Emotion RecognitionPoint Tracking	—Unverified

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All datasets TAP-Vid-DAVIS PointOdyssey TAP-Vid-DAVIS-First TAP-Vid-Kinetics TAP-Vid-Kinetics-First TAP-Vid-RGB-Stacking Perception Test TAP-Vid

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LocoTrack-B	Average Jaccard	69.4	—	Unverified
2	BootsTAPIR	Average Jaccard	66.2	—	Unverified
3	CoTracker	Average Jaccard	65.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PIPs++	Survival	50.47	—	Unverified
2	PIPs+	Survival	49.88	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LocoTrack-B	Average Jaccard	64.8	—	Unverified
2	CoTracker	Average Jaccard	62.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BootsTAPIR	Average Jaccard	61.4	—	Unverified
2	LocoTrack-B	Average Jaccard	59.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LocoTrack-B	Average Jaccard	52.3	—	Unverified
2	CoTracker	Average Jaccard	48.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BootsTAPIR	Average Jaccard	72.4	—	Unverified
2	LocoTrack-B	Average Jaccard	70.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Static Baseline	Average Jaccard	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PIPs++	MTE	4.6	—	Unverified