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An Efficient Fundamental Matrix Estimation for Moving Object Detection

Authors: Yeongyu Choi, Ju H. Park, S. M. Lee, Ho-Youl Jung


In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values.

Keywords: Corner detection, optical flow, epipolar geometry, RANSAC.

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[1] K. Yamaguchi, K. Takeo, and N. Yoshiki, “Vehicle Ego-motion Estimation and Moving Object Detection using a Monocular Camera," Proceedings of IEEE International Conference on Pattern Recognition, Vol. 4, pp. 610-613, 2006.
[2] C. Harris, and S. Mike, “A Combined Corner and Edge Detector," Proceedings of Alvey vision conference, Vol. 15, No. 50, 1988.
[3] R. Hartley. “In Defense of the Eight-point Algorithm," IEEE Transactions on pattern analysis and machine intelligence, Vol. 19, No. 6, pp. 580-593, 1997.
[4] M. Fischler, and R. Bolles, “Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Communications of the ACM, Vol. 24, No. 6, pp. 381-395, 1981.
[5] R. Hartley, and A. Zisserman, “Multiple View Geometry in computer vision,” 2000.