3D Pose Tracking Using Particle Filter with Back Projection-Based Sampling

Abstract

This paper explores pose tracking with a 3D object model using a particle filter. In tracking an object with a particle filter, a problem of high dimensional solution space often appears. In a huge space, a search process is tedious and falls foul of the exponential computational complexity. We propose the back projection-based sampling with a depth map from a stereo camera, which reduces the solution space significantly. In addition, we treat a method that constructs the 3D model used in this paper. Results are shown for tracking the pose of several objects.