A new way to use hidden Markov models for object tracking in video sequences
Sébastien Lefèvre, Eric A. Bouton, Thierry Brouard, Nicole Vincent · 2004
In this paper, we are dealing with color object tracking. We propose to use hidden Markov models in a different way as classical approaches. Indeed, we use these mathematical tools to model the object in the spatial domain rather than in the temporal domain. Besides in order to manage multidimensional (color) data, multidimensional hidden Markov models are involved. Object learning step is performed using the GHOSP algorithm whereas object tracking step is done by approximate object position prediction and then precise object position localisation. This last step can be seen as an object recognition problem and will be solved using a method based on the forward algorithm.