Roberto Fraile and Steve Maybank
Computational Vision Group, Department of Computer Science, The University of Reading, UK.
We present an application of algorithmic complexity to pose refinement. Given the image data, the camera calibration and a hypothesis in the form of a vehicle trajectory, an evaluation function is defined that allows a search for the best hypothesis. The function is simply the length of the data after it has been compressed using the hypothesis. The hypothesis at which the evaluation function attains a minimum is chosen as the best available. The effectiveness of this method for choosing trajectories is assessed experimentally.