Low-cost framework for 3D reconstruction and track detection of the railway network using video data

Abstract

3D reconstruction from video data is proposed for mapping the Egyptian railway network. A generic framework is proposed and applied based on video data. This framework mainly passes through three main stages. The pre-processing stage contains system implementation, data acquisition, the keyframes extraction, and camera calibration. The second stage is the generation of the 3D reconstruction models from a set of two-dimensional images. This stage is mainly based on the feature correspondence process and Structure from Motion (SfM) techniques. The 3D coordinates of the feature points and relative camera motion parameters are refined using the bundle adjustment algorithms to generate an accurate 3D reconstruction model. Many models are reconstructed based on different parameters/scenarios to evaluate the most accurate model. The best 3D model was selected and used to generate an orthogonal photo. Finally, the third stage is the track detection framework which uses the generated orthogonal image for automatic detection of railway tracks. The framework is tested on a dataset of a railway network with an approximate length of 20 km. The accuracy of the results is compared with field survey data conducted in the same area using conventional surveying instruments. From the results, the recommended train speed for reasonable video data collection is 10 kph with an overlap percentage of 95 %. The RMSE of the extracted track width is 0.03 m, and it contributes to the efficiency of our approach in the reconstruction of 3D surfaces compared to the recommended procedures in traditional techniques.