Metaverse platforms are becoming an increasingly popular form of collaboration within virtual worlds. Such platforms provide users with the ability to build virtual worlds that can simulate real-life experiences through different social activities. In the paper, we introduce a novel platform that provides assistive tools for building an educational experience in virtual worlds and overcoming the boundaries caused by pandemic situations. Therefore, the authors developed a high-level software architecture and design for a metaverse platform named VoRtex. VoRtex is primarily designed to support collaborative learning activities with the virtual environment. It is designed to support educational standards and it represents an open-source accessible solution developed using modern technology stack and metaverse concepts. For this study, we conducted a comparative analysis of the implemented VoRtex prototype and some popular virtual world platforms using Mannien’s matrix. Afterwards, based on the comparison, we evaluated the potential of the chosen virtual world platform and the VoRtex platform for online education. After an interactive demonstration of the VoRtex platform, participants were asked to fill out a questionnaire form. The aim was to enable participants to identify the main advantages of online teaching using the VoRtex platform. Finally, the authors analyzed benefits and disadvantages of collaborative learning between the metaverse platform and real-world classroom sessions.

This paper discusses the integration of three-dimensional (3D) geographic information systems (GIS) and video surveillance systems using augmented reality (AR) techniques. The motivation for this integration is to overcome problems faced by conventional video surveillance systems. Explicit information concerning which camera currently monitors what area in such systems is missing; therefore, insight into the situation depends heavily on the operator’s training and experience. To ensure the complete coordination and monitoring of a situation in a system with multiple cameras, it is necessary to introduce a single reference system. GIS arises as a natural solution because it not only provides a solid ground truth but also provides semantic information that can be highly important in certain video surveillance applications. To integrate information into a GIS application, that information must be georeferenced. Based on our previous research regarding the addition of georeferencing information to surveillance video, this paper introduces models that can be applied to help integrate video and GIS. With an analogy to Milgram’s continuum between the real world and virtual reality, and analogous to the augmented reality and augmented virtuality in Milgram’s continuum, two models of integration are defined here: GIS-augmented video and video-augmented GIS. Then, we define the architecture of GIS-based video surveillance based on these proposed integration models, and finally, a prototype is implemented. The implemented prototype serves as a basis for analysing possible applications of real-world systems based on the integration of GIS and video.

Registration in augmented reality is a process that merges virtual objects generated by a computer with real-world images captured by a camera. In this article, we present a method for registration of geospatial data applicable to outdoor video surveillance systems consisting of several PTZ cameras. PTZ is an abbreviation for pan-tilt-zoom, and in the terminology of video surveillance it indicates cameras that can rotate in the horizontal (pan) and vertical planes (tilt) and change their level of magnification (zoom). Registration is based on transforming these relative camera view parameters into the absolute position, orientation and field of view required by the three-dimensional geographic information systems (3D GISs). Once the 3D GIS and camera views are aligned, it is possible to identify geospatial objects from the camera image, as well as to overlap the virtual scene with the real one. In addition, inverse transformation of the view parameters allows for selecting and pointing the appropriate camera to some geo-referenced feature or event. Based on the proposal, we developed GeoScopeAVS, a GIS-based system for augmented video surveillance and suggested application of such a system in emergency situation management and urban planning.

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.