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Ground Plane Estimation using a Hidden Markov Model

Ralf Dragon and Luc Van Gool


We focus the problem of estimating the ground plane orientation and location in monocular video sequence from a moving observer. Our only assumptions are that that the 3D ego motion t and the ground plane normal n are orthogonal, and that n and t are smooth over time. We formulate the problem as a state-continuous Hidden Markov Model (HMM) where the hidden state contains t and n 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.

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  author = {Ralf Dragon and Luc Van Gool},
  title = {Ground Plane Estimation using a Hidden Markov Model},
  booktitle = {CVPR},
  year = {2014},
  keywords = {}