An efficient Rao-Blackwellized particle filter for object tracking

In this paper we present a technique for the tracking of textured almost planar object. The target is modeled as a noisy planar cloud of points. The tracking is led with an appropriate non linear stochastic filter. The particular system that we devised is conditionally Gaussian and can be efficiently implemented through variance reduction principle known as Rao-Blackwellisation. Our model allows also to melt a correlation measurements with dynamic model estimated from the images. Such a cooperation within a stochastic filtering framework allows the tracker to be robust to occlusions and target's unpredictable changes of speed and direction. We demonstrate the efficiency of the tracker on different types of real world sequences.