Stereo Based 3D Tracking and Scene Learning, employing Particle Filtering within EM

Trausti Kristjansson, Hagai Attias and John Hershey

Abstract

We present a generative probabilistic model for 3D scenes with stereo views. With this model, we track an object in 3 dimensions while simultaneously learning its appearance and the appearance of the background. By using a generative model for the scene, we are able to aggregate evidence over time. In addition, the probabilistic model naturally handles sources of variability. For inference and learning in the model, we formulate an Expectation Maximization (EM) algorithm where Rao-Blackwellized Particle filtering is used in the E step. The use of stereo views of the scene is a strong source of disambiguating evidence and allows rapid convergence of the algorithm. The update equations have an appealing form and as a side result, we give a generative probabilistic interpretation for the Sum of Squared Differences (SSD) metric known from the field of Stereo Vision.

Stereo Based 3D Tracking and Scene Learning, employing Particle Filtering within EM