Quadrangle Detection Based on A Robust Line Tracker Using Multiple Kalman Models - Hung Kwun Fung; Kin Hong Wong (Journal of ICT Research and Applications, 2013)
Quadrangle and line tracking are essential for many real world applications of computer vision. In this paper, we propose a computationally efï¬cient line tracker that can robustly and accurately track lines in an image. We use a multiple-model-Kalman ï¬lter (MMKF) scheme, which can handle line tracking accurately and robustly. The basic idea is to run N multiple sub-Kalman ï¬lters in parallel. Each ï¬lter is conï¬gured to use a different state transition model. All the ï¬lters are updated by the measurement at the same time following the conventional Kalman ï¬lter update process. The ï¬nal prediction is a combination of outputs from all the Kalman ï¬lter 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.