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Tracking nonparameterized object contours in video.

Hieu Tat Nguyen1, Marcel Worring, Rein van den Boomgaard

  • 1Intelligent Sensory Information Systems Group, Faculty of Science, University of Amsterdam, NL-1098 SJ, Amsterdam, The Netherlands. tat@science.uva.nl

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study introduces a novel video contour tracking method using edge detection and topographical distance for improved accuracy. The approach accounts for object motion to effectively handle background clutter, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Contour tracking in video is crucial for object analysis.
  • Existing methods struggle with background clutter and motion.
  • Edge detection and segmentation are key components.

Purpose of the Study:

  • To develop an advanced contour tracking method for video analysis.
  • To improve robustness against background clutter and object motion.
  • To integrate edge detection with topographical distance for enhanced segmentation.

Main Methods:

  • Utilizing the inverted distance transform of edge maps for contour detection.
  • Formulating watershed segmentation as a minimization problem based on topographical distance.
  • Combining current edge information with previous frame predictions for contour determination.

Related Experiment Videos

  • Incorporating object motion compensation to mitigate background clutter effects.

    • Main Results:

    • The proposed method effectively detects contours by combining edge maps and predicted contours.
    • Object motion compensation successfully removes spurious edges caused by background clutter.
    • Experimental results demonstrate superior performance compared to existing contour tracking approaches.

    Conclusions:

    • The novel contour tracking method offers significant advantages in video analysis.
    • The integration of topographical distance and motion compensation enhances accuracy and robustness.
    • This approach provides a more reliable solution for tracking contours in complex video scenes.