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Generally, subspace learning based methods such as the Incremental Visual Tracker (IVT) have been shown to be quite effective for visual tracking problem. However, it may fail to follow the target when it undergoes drastic pose or illumination changes. In this work, we present a novel tracker to enhance the IVT algorithm by employing a multicue based adaptive appearance model. First, we carry out the integration of cues both in feature space and in geometric space. Second, the integrationdoi:10.1155/2014/343860 fatcat:wsp7qywl7jbwzanzdti72fdaie