International Journal of Computer Vision

, Volume 2, Issue 4, pp 373–394 | Cite as

Recovery of the 3-D location and motion of a rigid object through camera image (An Extended Kalman Filter Approach)

  • J. J. Wu
  • R. E. Rink
  • T. M. Caelli
  • V. G. Gourishankar


This paper deals with the problem of locating a rigid object and estimating its motion in three dimensions. This involves determining the position and orientation of the object at each instant when an image is captured by a camera, and recovering the motion of the object between consecutive frames.

In the implementation scheme used here, a sequence of camera images, digitized at the sample instants, is used as the initial input data. Measurements are made of the locations of certain features (e.g., maximum curvature points of an image contour, corners, edges, etc.) on the 2-D images. To measure the feature locations a matching algorithm is used, which produces correspondences between the features in the image and the object.

Using the measured feature locations on the image, an algorithm is developed to solve the location and motion problem. The algorithm is an extended Kalman filter modeled for this application.


Kalman Filter Feature Location Implementation Scheme Matching Algorithm Extend Kalman Filter 
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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Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • J. J. Wu
  • R. E. Rink
  • T. M. Caelli
  • V. G. Gourishankar

There are no affiliations available

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