Abstract
This paper presents a method for detecting moving objects in two temporal succeeding images by calculating the fundamental matrix and the radial distortion and therefore, the distances from points to epipolar lines. In static scenes, these distances are a result of noise and/or the inaccuracy of the computed epipolar geometry and lens distortion. Hence, we are using these distances by applying an adaptive threshold to detect moving objects using views of a camera mounted on a Micro Unmanned Aerial Vehicle (UAV). Our approach uses a dense optical flow calculation and estimates the epipolar geometry and radial distortion. In addition, a dedicated approach of selecting point correspondences that suits dense optical flow computations and an optimization–algorithm that corrects the radial distortion parameter are introduced. Furthermore, the results on distorted ground truth datasets show a good accuracy which is outlined by the presentation of the performance on real–world scenes captured by an UAV.
The research leading to these results was funded by the KIRAS security research program of the Austrian Ministry for Transport, Innovation and Technology (www.kiras.at).
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Maier, J., Ambrosch, K. (2011). Distortion Compensation for Movement Detection Based on Dense Optical Flow. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_16
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DOI: https://doi.org/10.1007/978-3-642-24028-7_16
Publisher Name: Springer, Berlin, Heidelberg
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