Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms.

This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. There is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.