  A robust visual tracking system requires an object appearance model that is
able to handle occlusion, pose, and illumination variations in the video
stream. This can be difficult to accomplish when the model is trained using
only a single image. In this paper, we first propose a tracking approach based
on affine subspaces (constructed from several images) which are able to
accommodate the abovementioned variations. We use affine subspaces not only to
represent the object, but also the candidate areas that the object may occupy.
We furthermore propose a novel approach to measure affine subspace-to-subspace
distance via the use of non-Euclidean geometry of Grassmann manifolds. The
tracking problem is then considered as an inference task in a Markov Chain
Monte Carlo framework via particle filtering. Quantitative evaluation on
challenging video sequences indicates that the proposed approach obtains
considerably better performance than several recent state-of-the-art methods
such as Tracking-Learning-Detection and MILtrack.
