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Video object tracking is the process of locating one or several moving objects in time by the use of optical cameras. In this paper, an algorithm for object tracking by the use of particle filtering is presented. The algorithm employs fuzzy techniques for feature estimation. The algorithm handles color video image sequences from a stationary camera under changing illumination conditions. The proposed algorithm successfully tracks multiple objects by the use of an adaptive Gaussian mixture model for background modeling and a sequential Monte-Carlo-based tracking algorithm. Various fuzzy distance measures have been applied and compared for the estimation of the object location.