180 likes | 288 Views
Estimating Human Body Configurations using Shape Context Matching. Greg Mori and Jitendra Malik. Problem. Approach: Exemplar-based Matching. Set of stored exemplars with hand-labeled keypoints Obtain sample points Deformable matching to exemplars:
E N D
Estimating Human Body Configurations using Shape Context Matching Greg Mori and Jitendra Malik
Approach: Exemplar-based Matching • Set of stored exemplars with hand-labeled keypoints • Obtain sample points • Deformable matching to exemplars: • Shape context matching to get correspondences • Kinematic chain deformation model • Estimate 3D body configuration
Shape Context Count the number of points inside each bin, e.g.: Count = 4 ... Count = 10 • Compact representation of distribution of points relative to each point
Comparing Shape Contexts Compute matching costs using Chi Squared distance: Recover correspondences by solving linear assignment problem with costs Cij [Jonker & Volgenant 1987]
Deformable Matching • Kinematic chain-based deformation model • Use iterations of correspondence and deformation • Keypoints on exemplars are deformed to locations on query image
Estimate 3D Body Configuration [Taylor ’00] • Known: • Relative lengths of body segments • (x,y) Image locations of keypoints • “closer endpoint” labels for each segment • Scaled orthographic camera model • Solve for 3D locations of keypoints up to some scale factor • Scale factor can be estimated automatically
Multiple Exemplars • Parts-based approach • Use a combination of keypoints/whole limbs from different exemplars • Reduces the number of exemplars needed • Compute a matching cost for each limb from every exemplar • Compute pairwise “consistency” costs for neighbouring limbs • Use dynamic programming to find best K configurations