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Motion estimation based on time-sequentially sampled imagery.

M A Rahgozar1, J P Allebach

  • 1Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study explores motion estimation using time-sequentially sampled image data, comparing centroid-displacement and Fourier methods. Results offer practical guidance for motion parameter determination in tracking applications.

Area of Science:

  • Computer Vision
  • Digital Signal Processing
  • Image Analysis

Background:

  • Motion estimation is crucial for computer vision and video coding.
  • Existing algorithms primarily use frame-instantaneously sampled data.
  • The impact of sampling strategies on motion estimation is underexplored.

Purpose of the Study:

  • Investigate motion estimation with time-sequentially sampled image data.
  • Compare centroid-displacement and Fourier-based motion estimation approaches.
  • Evaluate performance against conventional frame-instantaneously sampled data.

Main Methods:

  • Developed and evaluated centroid-displacement and Fourier-based motion estimators.
  • Utilized a simulated tracking environment for performance analysis.

Related Experiment Videos

  • Tested estimators under violated underlying assumptions.

    • Main Results:

    • Demonstrated performance characteristics of different motion estimation methods with time-sequential sampling.
    • Provided extensive numerical results in a simulated tracking scenario.
    • Identified empirical rules for selecting estimator parameter values.

    Conclusions:

    • Time-sequential sampling impacts motion estimation performance.
    • Centroid-displacement and Fourier methods show varying effectiveness.
    • Empirical guidelines aid parameter selection for motion estimation in tracking.