Tracking a Dynamic Set of Feature Points

Abstract

This paper presents a model-based algorithm for tracking feature points over a long sequence of monocular noisy images with the ability to include new feature points detected in successive frames. The trajectory for each feature point is modeled by a simple kinematic motion model. A probabilistic Data Association Filter is first designed to estimate the motion between two consecutive frames. A matching algorithm then identifies the corresponding point to subpixel accuracy and an Extended Kalman Filter (EKF) is employed to continually track the feature point. An efficient way to dynamically include new feature points from successive frames into a tracking list is also addressed. Tracking results for several image sequences are given.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1994
Accession Number
ADA285124

Entities

People

  • Rama Chellappa
  • Yi-sheng Yao

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computer Vision
  • Coordinate Systems
  • Data Association
  • Electrical Engineering
  • Filters
  • Grids
  • Image Registration
  • Interpolation
  • Kalman Filters
  • Mathematical Filters
  • Sequences
  • Statistical Algorithms
  • Three Dimensional
  • Trajectories
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.