An algorithm to estimate camera motion from the progressive deformation of a tracked contour in the acquired video stream has been previously proposed. It relies on the fact that two views of a plane are related by an affinity, whose 6 parameters can be used to derive the 6 degrees-of-freedom of camera motion between the two views. In this paper we evaluate the accuracy of the algorithm. Monte Carlo simulations show that translations parallel to the image plane and rotations about the optical axis are better recovered than translations along this axis, which in turn are more accurate than rotations out of the plane. Concerning covariances, only the three less precise degrees-of-freedom appear to be correlated. In order to obtain means and covariances of 3D motions quickly on a working robot system, we resort to the Unscented Transformation (UT) requiring only 13 samples per view, after validating its usage through the previous Monte Carlo simulations. Two sets of experiments have been performed: short-range motion recovery has been tested using a Sta{\"u}bli robot arm in a controlled lab setting, while the precision of the algorithm when facing long translations has been assessed by means of a vehicle-mounted camera in a factory floor. In the latter more unfavourable case, the obtained errors are around 3%, which seems accurate enough for transferring operations.


computer vision.

Author keywords

egomotion estimation, active contours, precision analysis, unscented transformation

Scientific reference

G. Alenyà and C. Torras. Camera motion estimation by tracking contour deformation: Precision analysis. Image and Vision Computing, 28(3): 474-490, 2010.