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A New Approach to the Use of Edge Extremities for Model-based Object Tracking

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4 Author(s)
Youngrock Yoon ; Robot Vision Lab Purdue University West Lafayette, IN 47907 U.S.A. ; A. Kosaka ; Jae Byung Park ; A. C. Kak

This paper presents a robust model-based visual tracking algorithm that can give accurate 3D pose of a rigid object. Our tracking algorithm uses an incremental pose update scheme in a prediction-verification framework. Extended Kalman filter is used to update the pose of a target incrementally to minimize the error between the expected map of the target model and the corresponding gradient edge in the image space. The main contributions of this paper include: 1) A novel approach to how we use the two extremities of straight-lines as features. By taking into account the measurement uncertainties associated with the locations of the extracted extremities of the straight-line, our approach can compare correctly two straight-lines of different lengths. 2) Our use of a test of mean criterion for initiating backtracking and our use of a variable threshold on the output of this criterion that makes nil-matching more effective. We have tested our tracking algorithm with image sequences containing highly cluttered backgrounds. The system successfully tracks objects even when they are highly occluded.

Published in:

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

Date of Conference:

18-22 April 2005