Learning Non-coplanar Scene Models by Exploring the Height Variation of Tracked Objects

Abstract

AbstractIn 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).KeywordsCamera calibrationnon-coplanar planesregion segmentationmotion varietydepth and altitude estimation