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Published byMeagan Alice Baker Modified over 9 years ago
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Pyramidal Implementation of Lucas Kanade Feature Tracker Jia Huang Xiaoyan Liu Han Xin Yizhen Tan
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Abstract Introduction Tracking algorithm Lucas-Kanade algorithm Iterative implementation Tracking features analysis Feature lost Feature selection
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Objective For a given point u in image A, find its corresponding location v = u + d in image B. Image A Image B d
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Residual function and Window size To find the location Minimize residual function: : Integration window size Small integration windowHigher accuracy Larger integration windowHigher robustness Nature tradeoff:
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Pyramid Implementation of LK algorithm Calculate a set of pyramid representations of original image Apply traditional tracking algorithm for each level Results of current iteration is propagated to next iteration Key point: the same window size is used for each level Top ViewSide View
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Lucas-Kanade algorithm(1) At the level L, we define images A and B:
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Lucas-Kanade algorithm(2) At the optimum, the first derivative of After first order Taylor expansion Components in the equation above
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Lucas-Kanade algorithm(3) Two derivative images are expressed: With these notation, we can get: The optimum optical flow vector is
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Pyramidal diagram Inner loop: K-level K initialized to 1, assume that the previous computations from iterations 1,2,...,k-1 provide an initial guess The new translated image according to Iterative scheme of LK algorithm(1)
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Iterative scheme of LK algorithm(2) The goal: to compute the residual pixel motion vector, that minimizes the error function Image mismatch vector, where the image difference delta I k defined as: New pixel displacement guess is computed for the next iteration step k+1:
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Iterative scheme of LK algorithm(3) On average, 5 iterations are enough At the 1st iteration (k=1), the initial guess is set to zero The final solution for the optical flow vector is Outer loop: L-level The vector d is propagated to the next level L-1 and overall procedure is repeated L-1, L- 2, …, 0
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Declaring a Feature Lost Several cases of lost feature the point falls outside of the image image patch around the tracked point varies too much between image A and image B too large displacement How to solve it combine a traditional tracking approach with an affine image matching
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Feature Lost Example(1) Image A Image B
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Feature Lost Example(2) Image A Image B
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Feature Selection Intuitive To select the point u on image A good to track. Process steps: Compute the G matrix and λ m Call λ max the maximum value of λ m Retain the pixels that have a λ m value larger than a percentage of λ max Retain the local max. pixels Keep the subset of those pixels so that the minimum distance between pixels is larger than a threshold
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Example of LK Feature Tracking Image A Image B
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More Examples Image B Image A
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Summary Lucas-Kanade Feature Tracker is one of the most popular versions of two-frame differential methods for motion estimation Iterative implementation of the Lucas- Kanade optical flow computation provides sufficient local tracking accuracy.
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Thanks for your attention Any question?
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