Multiple kernel two-step tracking

In tracking tasks, representing a target region as a weighted histogram has opened possibilities which led to excellent re-sults, as Mean Shift or Camshift algorithms. This representa-tion is extracted from the image by giving weights with ker-nels and it depends on the properties of the kernels. By a first order Taylor approximation of the histograms it is possible to perform a tracking using several kernels, interpreted as differ-ent sources of information. This representation improves the possibilities and gives more flexibility when facing problems of tracking, as occlusions, model variance or projective defor-mations of the image. In this paper we use this multi-kernel model representation to perform a simultaneous tracking of the entire object and also of each different part individually. This is performed in a new two-step process. In the first step we perform the mul-tikernel estimation and in a second step we update the model representation taking into account single kernel estimations, representing the local movement of each part. From a probabilistic view of the Matusita metric we analyze the usefulness of this method against partial occlusions and some projective transformations like zooms or 3D rotations and articulated movements. 1