Search Results (2)

With the comprehensive promotion of digital construction in China, cameras scattered throughout the country are of great significance in obtaining first-hand data. However, their potential role is limited due to the lack of georeference information on current surveillance cameras. Provided surveillance camera images and real scenes are combined and given georeference information, this problem can be solved, allowing cameras to generate significant social benefits. This article proposed an accurate registration method based on misalignment calibration and least squares matching between real scene and surveillance camera images to address this issue. Firstly, it is necessary to convert the navigation coordinate system from which cameras obtain data to the photogrammetric coordinate system and then solve for the misalignment and internal orientation elements of the camera. Then, accurate registration is achieved using the least squares matching on pyramid images. The experiment obtained surrounding image data of two common scenes with lens pitch angles of 45°, 55°, 65°, 75°, and 85° using the surveillance camera and obtained a 3D real scene model of each scene using a low-altitude aircraft. The experiment results show that the proposed method in this paper can achieve the expected goals of accurately matching real scene and surveillance camera images and assigning georeference information. Through extensive data analysis, the success rate and accuracy rate of registration are 98.1% and 97.06%, respectively. Full article

The integration of a surveillance camera video with a three-dimensional (3D) geographic information system (GIS) requires the georeferencing of that video. Since a video consists of separate frames, each frame must be georeferenced. To georeference a video frame, we rely on the information about the camera view at the moment that the frame was captured. A camera view in 3D space is completely determined by the camera position, orientation, and field-of-view. Since the accurate measuring of these parameters can be extremely difficult, in this paper we propose a method for their estimation based on matching video frame coordinates of certain point features with their 3D geographic locations. To obtain these coordinates, we rely on high-resolution orthophotos and digital elevation models (DEM) of the area of interest. Once an adequate number of points are matched, Levenberg–Marquardt iterative optimization is applied to find the most suitable video frame georeference, i.e., position and orientation of the camera. Full article