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Tracking Hands with Distance Transforms Dave Bargeron Noah Snavely
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The problem Input: A video with a (rigid) hand Output: A sequence of hand locations and orientations
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Approach 1.Generate hand templates for all possible orientations 2.Find the edges in each input image 3.For each edge image, find the template and location which minimizes the chamfer distance
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Step 1: Generate Templates 1.Create 3D hand model 2.Render in a set of orientations 3.Use depth buffer to find silhouette and contours
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Steps 2 + 3: Find the hand 1.Compute the distance transform of the edge image 2.Slide each template over the distance transform, compute the chamfer distance 3.Pick the template with the minimum chamfer distance
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Problems Large number of templates –(3211 rotations) x (3072 translations) x (5 scales) = 49,320,960 templates In a cluttered image: –Chamfer distance has many local optima –Global optimum may not be correct Solving each frame separately not a good idea
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Solution Part 1: Template Tree Coarse-to-fine search in parameter space
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Solution Part 2: Tracking Detect the hand in frame 0 For each frame k > 0: –Compute the most likely transition from state in frame k-1 Use chamfer distance as a likelihood Use transition probability (assumed Gaussian) as a prior –Use transition probabilities to prune branches of the search tree
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Results – Hand Detection InputEdge imageDistance transformOutput
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Results – Video
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Edge ImagesDistance Transforms
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Results – Video
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Extensions Better tracking –Use color in addition to shape –Use edge orientations –More templates; allow for on-line generation for refinement More flexible tracking –Track deformable hand –Automatically determine hand parameters (e.g. finger length)
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References Björn Stenger’s Ph.D thesis –http://mi.eng.cam.ac.uk/~bdrs2/papers/stenger04_thesis.pdfhttp://mi.eng.cam.ac.uk/~bdrs2/papers/stenger04_thesis.pdf B. Stenger, et. al. “Filtering Using a Tree-Based Estimator.” ICCV 2003. Pedro Felzenszwalb and Dan Huttenlocher. “Distance Trasforms of Sampled Functions.”
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