Abstract
In this paper, we present a novel method to overcome the common constraint of traditional camera calibration methods of surveillance systems where all objects move on a single coplanar ground plane. The proposed method estimates a scene model with non-coplanar planes by measuring the variation of pedestrian heights across the camera FOV in a statistical manner. More specifically, the proposed method automatically segments the scene image into plane regions, estimates a relative depth and estimates the altitude for each image pixel, thus building up a 3D structure with multiple non-coplanar planes. By being able to estimate the non-coplanar planes, the method can extend the applicability of 3D (single or multiple camera) tracking algorithms to a range of environments where objects (pedestrians and/or vehicles) can move on multiple non-coplanar planes (e.g. multiple levels, overpasses and stairs).
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Yin, F., Makris, D., Orwell, J., Velastin, S.A. (2011). Learning Non-coplanar Scene Models by Exploring the Height Variation of Tracked Objects. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_21
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DOI: https://doi.org/10.1007/978-3-642-19318-7_21
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