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Tracking an object without any prior information regarding its appearance is a challenging problem. Modern tracking algorithms treat tracking as a binary classification problem between the object class and the background class. The binary classifier can be learned offline, if a specific object model is available, or online, if there is no prior information about the object's appearance. In this paper, we propose the use of online distance metric learning in combination with nearest neighbor classification for object tracking. We assume that the previous appearances of the object and the background are clustered so that a nearest neighbor classifier can be used to distinguish between the new appearance of the object and the appearance of the background. In order to support the classification, we employ a distance metric learning (DML) algorithm that learns to separate the object from the background. We utilize the first few frames to build an initial model of the object and the background and subsequently update the model at every frame during the course of tracking, so that changes in the appearance of the object and the background are incorporated into the model. Furthermore, instead of using only the previous frame as the object's model, we utilize a collection of previous appearances encoded in a template library to estimate the similarity under variations in appearance. In addition to the utilization of the online DML algorithm for learning the object/background model, we propose a novel feature representation of image patches. This representation is based on the extraction of scale invariant features over a regular grid coupled with dimensionality reduction using random projections. This type of representation is both robust, capitalizing on the reproducibility of the scale invariant features, and fast, performing the tracking on a reduced dimensional space. The proposed tracking algorithm was tested under challenging conditions and achieved state-of-the art- performance.