Visual Tracking Via Subspace Motion Model

In this paper we propose a novel motion model for visual tracking, which can be used to parameterize instantaneous image motion caused by both object and camera movements. Our approach is inspired by the subspace theory of image motion, that is, for a rigid object imaged by a projective camera, the displacements matrix of its trajectories over a short period of time should approximately lie in a low-dimensional subspace with a certain rank upper bound. We adopt this subspace as the state transition space in particle filtering, by which we can obtain a state vector with alterable number of dimensions. The dimension number as well as the sampling weight for each dimension at each moment can be determined by the rank of the subspace. In this way the particle distribution will be more coincide with the probability of the object state. Based on the subspace motion model, we derive a new visual tracking approach that can handle more complicated cases. The subspace is also used to discriminate new feature points of the object for adaptively updating the motion model. Experimental results and comparisons demonstrate the effectiveness of the proposed method.