Towards Euclidean auto-calibration of stereo camera arrays

Multi-camera networks are becoming ubiquitous in a variety of applications related to medical imaging, education, entertainment, autonomous vehicles, civil security, defense etc. The foremost task in deploying a multi-camera network is camera calibration, which usually involves introducing an object with known geometry into the scene. However, most of the aforementioned applications necessitate non-intrusive automatic camera calibration. To this end, a class of camera auto-calibration methods imposes constraints on the camera network rather than on the scene. In particular, the inclusion of stereo cameras in a multi-camera network is known to improve calibration accuracy and preserve scale. Yet most of the methods relying on stereo cameras use custom-made stereo pairs, and such stereo pairs can definitely be considered imperfect; while the baseline distance can be fixed, one cannot guarantee the optical axes of two cameras to be parallel in such cases. In this paper, we propose a characterization of the imperfections in those stereo pairs with the assumption that such imperfections are within a considerably small, reasonable deviation range from the ideal values. Once the imperfections are quantified, we use an auto-calibration method to calibrate a set of stereo cameras. We provide a comparison of these results with those obtained under parallel optical axes assumption. The paper also reports results obtained from the utilization of synthetic visual data

Three-dimensional scene recovery for measuring sighting distances of rail track assets from monocular forward facing videos

Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.EThOS - Electronic Theses Online ServiceGBUnited Kingdo