The mean-shift algorithm is an efficient technique for track- ing 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing or updating scale while tracking blobs that are changing in size. We adapt Linde- berg's theory of feature scale selection based on local max- ima of differential scale-space filters to the problem of se- lecting kernel scale for mean-shift blob tracking. We show that a difference of Gaussian (DOG) mean-shift kernel en- ables efficient tracking of blobs through scale space. Using this kernel requires generalizing the mean-shift algorithm to handle images that contain negative sample weights.