Advances in Computer Vision pp 181-190 | Cite as

# Computation of time-varying motion and structure parameters from real image sequences

Conference paper

## Abstract

We address the problem of robust estimation of motion and structure parameters, which describe an observer’s translation, rotation, and environmental layout (i.e. the relative depth of visible 3-d points) from noisy time-varying optical flow. Allowable observer motions include a moving vehicle and a broad class of robot arm motions. We assume the observer is a camera rigidly attached to the moving vehicle or robot arm, which moves along a smooth trajectory in a stationary environment. As the camera moves it acquires images at some reasonable sampling rate (say 30 images per second). Given a sequence of such images we analyze them to recover the camera’s motion and depth information for various surfaces in the environment. As the camera moves, with respect to some 3-d environmental point, the relative 3-d velocity that occurs is mapped (under perspective projection) onto the camera’s image plane as 2-d where \({\vec \upsilon _T} \) and \({\vec \upsilon _R}\) are the translational and rotational components of image velocity: and

**image motion. Optical flow**or**image velocity**is an infinitesimal approximation to this image motion. Since the camera moves relative to a scene we can compute image velocity fields at each time. Given the observer’s translation, \(\vec U\), and rotation, \(\vec \omega \), and the coordinates of a 3-d point, \(\vec P\), a non-linear equation that relates these parameters to the 2-d image velocity, \(\vec \upsilon \), at image point \(\vec Y\), where \(\vec Y\) is the perspective projection of \(\vec P\), is as follows [10]:$$\vec \upsilon \left( {\vec Y,t} \right) = {\vec \upsilon _T}\left( {\vec Y,t} \right) + {\vec \upsilon _R}\left( {\vec Y,t} \right)$$

(1)

$${\vec \upsilon _T}\left( {\vec Y,t} \right) = {A_1}\left( {\vec Y} \right)\vec u\left( {\vec Y,t} \right){\left\| {\vec Y} \right\|_2} and {\vec v_R}\left( {\vec Y,t} \right) = {A_2}\left( {\vec Y} \right)\vec \omega \left( t \right)$$

(2)

$${A_1} = \left( {\begin{array}{*{20}{c}}{ - 1}&0&{{y_1}} \\ 0&{ - 1}&{{y_2}} \end{array}} \right) and {A_2}\left( {\begin{array}{*{20}{c}} {{y_1}{y_2}}&{ - \left( {1 + y_1^2} \right)}&{{y_2}} \\ {\left( {1 + y_2^2} \right)}&{ - {y_1}{y_2}}&{{y_1}} \end{array}} \right)$$

(3)

## Keywords

Optical Flow Image Motion Confidence Measure Image Velocity Rotational Acceleration
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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## References

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## Copyright information

© Springer-Verlag/Wien 1997