@article {12475,
	title = {Robust Height Estimation of Moving Objects From Uncalibrated Videos},
	journal = {IEEE Transactions on Image Processing},
	volume = {19},
	year = {2010},
	month = {2010/08//},
	pages = {2221 - 2232},
	abstract = {This paper presents an approach for video metrology. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene based upon tracking moving objects that primarily lie on a ground plane. Using geometric properties of moving objects, a probabilistic model is constructed for simultaneously grouping trajectories and estimating vanishing points. Then we apply a single view mensuration algorithm to each of the frames to obtain height measurements. We finally fuse the multiframe measurements using the least median of squares (LMedS) as a robust cost function and the Robbins-Monro stochastic approximation (RMSA) technique. This method enables less human supervision, more flexibility and improved robustness. From the uncertainty analysis, we conclude that the method with auto-calibration is robust in practice. Results are shown based upon realistic tracking data from a variety of scenes.},
	keywords = {algorithms, Biometry, Calibration, EM algorithm, geometric properties, Geometry, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, least median of squares, least squares approximations, MOTION, motion information, multiframe measurements, Pattern Recognition, Automated, Reproducibility of results, Robbins-Monro stochastic approximation, robust height estimation, Sensitivity and Specificity, Signal Processing, Computer-Assisted, stochastic approximation, Subtraction Technique, tracking data, uncalibrated stationary camera, uncalibrated videos, uncertainty analysis, vanishing point, video metrology, Video Recording, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2010.2046368},
	author = {Jie Shao and Zhou,S. K and Chellapa, Rama}
}
@conference {12047,
	title = {Which shape from motion?},
	booktitle = {Sixth International Conference on Computer Vision, 1998},
	year = {1998},
	month = {1998/01/04/7},
	pages = {689 - 695},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In a practical situation, the rigid transformation relating different views is recovered with errors. In such a case, the recovered depth of the scene contains errors, and consequently a distorted version of visual space is computed. What then are meaningful shape representations that can be computed from the images? The result presented in this paper states that if the rigid transformation between different views is estimated in a way that gives rise to a minimum number of negative depth values, then at the center of the image affine shape can be correctly computed. This result is obtained by exploiting properties of the distortion function. The distortion model turns out to be a very powerful tool in the analysis and design of 3D motion and shape estimation algorithms, and as a byproduct of our analysis we present a computational explanation of psychophysical results demonstrating human visual space distortion from motion information},
	keywords = {3D motion estimation, affine shape, Algorithm design and analysis, Computer vision, distorted version, distortion function, human visual space distortion, HUMANS, Image motion analysis, image representation, Information analysis, Layout, Motion analysis, Motion estimation, motion information, Psychology, rigid transformation, SHAPE, shape estimation, shape representations, State estimation, visual space},
	isbn = {81-7319-221-9},
	doi = {10.1109/ICCV.1998.710792},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
