Kanade-Lucas-Tomasi (KLT) Feature Tracker
Kanade-Lucas-Tomasi (KLT) Feature Tracker
Kanade-Lucas-Tomasi (KLT) Feature Tracker
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Pyramidal feature tracking<br />
• Let gL =[gL x gLy ]T be an initial guess at level L<br />
( gL is available from level Lm to level L+1 )<br />
• Residual pixel displacement vector d L =[d L x dL y ]T<br />
that minimizes the new image matching error function ε L<br />
• Window size (2ω x +1)×(2ω y +1) is constant for all pyramid<br />
• gL is used to pre-translate the image patch in 2nd image J<br />
→ dL is small and therefore easy to compute using <strong>KLT</strong> algorithm<br />
Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
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