Abstract
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.
Highlights
Video surveillance systems have rapidly expanded due to the technology’s important role in traffic monitoring, crime prevention, security, and post-incident analysis [1]
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
The integration of video surveillance and 3D geographic information system (GIS) paves the way for new opportunities that were not possible with conventional surveillance systems [20]
Summary
Video surveillance systems have rapidly expanded due to the technology’s important role in traffic monitoring, crime prevention, security, and post-incident analysis [1]. One way to do that is to record the camera position, orientation, and field-of-view at the moment of frame capture, so that at a later time, the frame image can be projected into the virtual 3D GIS scene. Since measuring these parameters can be both inaccurate and complex, in this paper we will present a method for their indirect estimation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
