lecture21 - Motion Tracking Motion tracking Suppose we have...

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Motion Tracking
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Motion tracking Suppose we have more than two images How to track a point through all of the images? Feature Tracking Choose only the points (“features”) that are easily tracked How to find these features? – In principle, we could estimate motion between each pair of consecutive frames – Given point in first frame, follow arrows to trace out it’s path – Problem: DRIFT » small errors will tend to grow and grow over time—the point will drift way off course – windows where has two large eigenvalues Called the Harris Corner Detector
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Feature Detection
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Tracking features Feature tracking Compute optical flow for that feature for each consecutive H, I When will this go wrong? Occlusions—feature may disappear need mechanism for deleting, adding new features Changes in shape, orientation – allow the feature to deform Changes in color Large motions – will pyramid techniques work for feature tracking?
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Handling large motions L-K requires small motion If the motion is much more than a pixel, use discrete
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This note was uploaded on 06/12/2011 for the course CAP 5415 taught by Professor Staff during the Fall '08 term at University of Central Florida.

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lecture21 - Motion Tracking Motion tracking Suppose we have...

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