Two-Step Tracking by Parts Using Multiple Kernels
Brais Martínez, Luis Ferraz, Xavier Binefa · Conference on Artificial Intelligence Research and Development · 2006
This paper addresses the problem of tracking IR image sequences by using kernel weighted histograms. The work is performed over the basis of the multiple kernel tracking algorithm presented in [3]. We present a new, novel, two-step tracking method which allows a tracking of independent parts of the same object by giving a higher flexibility to the multiple kernel model. This is performed by a progressive approximation of the movement by first estimating the global displacement with a multi-kernel estimator in order to have enough robustness and then, in the second step, the residual displacements of each part. The outcome is a method yet robust to partial occlusions, articulated motions or projectivities over the image with an application to partial occlusion detection and model update.