Object tracking with adaptive multicue incremental visual tracker

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 integration directly depends on the dynamically-changing reliabilities of visual cues. These two aspects of our method allow the tracker to easily adapt itself to the changes in the context and accordingly improve the tracking accuracy by resolving the ambiguities. Experimental results demonstrate that subspace-based tracking is strongly improved by exploiting the multiple cues through the proposed algorithm.