Ground Plane Estimation using a Hidden Markov Model

We focus on the problem of estimating the ground plane orientation and location in monocular video sequences from a moving observer. Our only assumptions are that the 3D ego motion (t) over right arrow and the ground plane normal (n) over right arrow are orthogonal, and that (n) over right arrow and (t) over right arrow are smooth over time. We formulate the problem as a state-continuous Hidden Markov Model (HMM) where the hidden state contains (t) over right arrow and (n) over right arrow and may be estimated by sampling and decomposing homographies. We show that using blocked Gibbs sampling, we can infer the hidden state with high robustness towards outliers, drifting trajectories, rolling shutter and an imprecise intrinsic calibration. Since our approach does not need any initial orientation prior, it works for arbitrary camera orientations in which the ground is visible.