An Efficient Tracking System by Orthogonalized Templates
Sparse representation (SR)-based tracking systems have become popular in the past recent years for its effectiveness. However, the underlying assumption of these tracking systems is that the target appearance can be linearly represented by a sparse approximation over a set of templates and a residual term, which usually needs to solve ℓ1 norm minimization for many times and brings a heavy computational cost. This paper introduces an efficient tracking system by discovering orthogonalized templates. By orthogonalizing templates from previous frames and removing their correlation, we show that the sparsity of template weights is not necessary in target appearance modeling and thus a least squares regularization can be employed. We also decompose the residual term into two components in observation model to take occlusion cases into consideration. We demonstrate that, in comparison with the SR-based tracking systems that use ℓ1 learning, our tracking system is much more computationally efficient while getting an even better performance. Experiments on a variety of challenging video sequences demonstrate both the effectiveness and efficiency of the system.