ICIP 2006, Atlanta, GA

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Paper Detail

Session:Motion Tracking
Time:Wednesday, October 11, 09:40 - 12:20
Presentation: Poster
Topic: Motion Detection and Estimation: Parametric models for motion estimation
Authors: Brais Martínez; Universitat Autónoma de Barcelona 
 Luis Ferraz; Universitat Autónoma de Barcelona 
 Xavier Binefa; Universitat Autónoma de Barcelona 
 Jose Díaz-Caro; Ministry of Defense 
Abstract: In tracking tasks, representing a target region as a weighted histogram has opened possibilities which led to excellent results, as Mean Shift or Camshift algorithms. This representation is extracted from the image by giving weights with kernels 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 different sources of information. This representation improves the possibilities and gives more flexibility when facing problems of tracking, as occlusions, model variance or projective deformations 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 multikernel 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.