Abstract
This paper discusses the integration of a geographic information system (GIS) and moving objects in surveillance videos (“moving objects” hereinafter) by using motion detection, spatial mapping, and fusion representation techniques. This integration aims to overcome the limitations of conventional video surveillance systems, such as low efficiency in video searching, redundancy in video data transmission, and insufficient capability to position video content in geographic space. Furthermore, a model for integrating GIS and moving objects is established. The model includes a moving object extraction method and a fusion pattern for GIS and moving objects. From the established integration model, a prototype of GIS and moving objects (GIS–MOV) system is constructed and used to analyze the possible applications of the integration of GIS and moving objects.
Highlights
Video surveillance is conducted using images produced by finite cameras
Applications Based on the geographic information system (GIS)–MOV System we briefly introduce some applications based on the GIS and moving objects (GIS–MOV) system and moving objects and the virtual geographic scene
After analyzing the integration model and the results of the implementation of the GIS–MOV system, we believe that, compared with the integration of GIS and video image, the integration of GIS and moving objects has the following advantages: (1) providing a video-augmented GIS information representation pattern in which the virtual geographic scene is enhanced by the moving objects; (2) reducing the amount of data required for the fusion of GIS and video; (3) allowing for a flexible selection of video foreground and background represented in GIS; (4) efficiently and intensively representing moving objects in the geographic space; (5) increasing the spatial positioning accuracy of moving objects
Summary
Video surveillance is conducted using images produced by finite cameras. Millions of cameras are collecting massive amounts of video data on a daily basis [1]. Studies on V-GIS disregarded the two major disadvantages of video data, namely, the massive amount of data and the sparse distribution of high-value information. Both of these disadvantages redound to practical problems, such as slow video browsing, low-efficiency manual retrieval, and video data transmission redundancy. [7] refined the theory posited by Milgram and Kishino through establishing a continuum that contains two models of integration for geospatial video and 3D GIS: GIS-augmented video and video-augmented GIS Drawing on these studies, our research aimed to define a model for integrating GIS and moving objects and implement a prototype based on this integration.
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