Quadrangle Detection Based on A Robust Line Tracker Using Multiple Kalman Models

Hung Kwun Fung, Kin Hong Wong

Abstract


Quadrangle and line tracking are essential for many real world applications of computer vision. In this paper, we propose a computationally efficient line tracker that can robustly and accurately track lines in an image. We use a multiple-model-Kalman filter (MMKF) scheme, which can handle line tracking accurately and robustly. The basic idea is to run N multiple sub-Kalman filters in parallel. Each filter is configured to use a different state transition model. All the filters are updated by the measurement at the same time following the conventional Kalman filter update process. The final prediction is a combination of outputs from all the Kalman filter modules. After lines are detected, we developed a scheme to merge the lines together to become suitable quadrangles. The experimental result shows that the proposed system can track lines and quadrangle robustly in real time. The result is useful in shape detection and should be suitable for building many mobile projector applications.

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References


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DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2013.7.2.3

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