- Conference Paper
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 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 in which the ground is visible. Show more
Book title2014 IEEE Computer Society Conference on computer vision and Pattern Recognition (CVPR 2014)
Pages / Article No.
SubjectGround plane; Visual odometry; Hidden markov model; Visual gyroscope
Organisational unit03514 - Van Gool, Luc / Van Gool, Luc
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