Efficient hyperplane tracking by intelligent region selection

The main aim of our work is to improve the accuracy of the hyperplane tracker of F. Jurie and M. Dhome (see IEEE Trans. Pattern Anal. and Machine Intelligence, vol.24, no.7, p.996-1000, 2002) for real-time template matching. As the computation time of the initialization of the algorithm depends on the number of points used for estimating the motion of the template, only a subset of points in the tracked template is considered. Traditionally, this subset is determined at random. We present three different methods for selecting points better suited for the hyperplane tracker. We also propose to incorporate color information by working with eigenintensities instead of gray-level intensities, which can greatly improve the estimation accuracy, but only entails a slight increase in computation time. We have carefully evaluated the performance of the proposed methods in experiments with real image sequences.