Real-time image-based tracking of planes using efficient second-order minimization

The tracking algorithm presented in this paper is based on minimizing the sum-of-squared-difference between a given template and the current image. Theoretically, amongst all standard minimization algorithms, the Newton method has the highest local convergence rate since it is based on a second-order Taylor series of the sum-of-squared-differences. However, the Newton method is time consuming since it needs the computation of the Hessian. In addition, if the Hessian is not positive definite, convergence problems can occur. That is why several methods use an approximation of the Hessian. The price to pay is the loss of the high convergence rate. The aim of this paper is to propose a tracking algorithm based on a second-order minimization method which does not need to compute the Hessian.