CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Discriminative Framework for Modelling Object Classes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Rao-blackwellized parts-constellation tracker
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
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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.