Mean-shift_Blob_Tracking_through_Scale_Space.pdf

CSE598G Robert Collins Mean-Shift Blob Tracking through Scale Space Robert Collins, CVPR’03 CSE598G Robert Collins Abstract ? Mean-shift tracking ? Choosing scale of kernel is an issue ? Scale-space feature selection provides inspiration ? Perform mean-shift with scale-space kernel to optimize for blob location and scale CSE598G Robert Collins Nice Property Δx = Σa K(a-x) w(a) (a-x) Σa K(a-x) w(a) [Σa ]H(a-x) w(a)x Running mean-shift with kernel K on weight image w is equivalent to performing gradient ascent in a (virtual) image formed by convolving w with some “shadow” kernel H. -c CSE598G Robert Collins Size Does Matter! Mean-shift is related to kernel density estimation , aka Parzen estimation, so choosing correct scale of the mean-shift kernel is important. Too big Too small CSE598G Robert Collins Size Does Matter Fixed-scale Tracking through scale space ± 10% scale adaptation Our Approach! CSE598G Robert Collins Some Approaches to Size Selection ?Choose one scale and stick with it. ?Bradski’s CAMSHIFT tracker computes principal axes and scales from the second moment matrix of the blob. Assumes one blob, little clutter. ?CRM adapt window size by +/- 10% and evaluate using Battacharyya coefficient. Although this does stop the window from growing too big, it is not sufficient to keep the window from shrinking too much. ?Comaniciu’s variable bandwidth methods. Computationally complex. ?Rasmussen and Hager: add a border of pixels around the window, and require that pixels in the window should look like the object, while pixels in the border should not. Center-surround CSE598G Robert Collins Scale-Space Theory CSE598G Robert Collins Basic idea: different scales are appropriate for describing different objects in the image, and we may not know the correct scale/size ahead of time. Scale Space CSE598G Robert Collins Scale Selection “Laplacian” operator. CSE598G Robert Collins Laplacian Operator and LoG Laplacian of Gaussian-filtered image Lapla