Geographic Video Object Knowledge Graph

This paper focuses on the structure, construction methods, and potential applications of the Geographic Video Object Knowledge Graph (GVOKG). The motivation for constructing GVOKG is that existing Video Knowledge Graphs (VKG) do not consider the geospatial relationships between images captured by different cameras and lack the analytical processing of the geographic spatiotemporal correlation of video objects. This results in incomplete content in the knowledge graph. Therefore, VKG needs to be spatially developed to support the analysis of the integrated expression and analysis of spatiotemporal processes of video objects. In this paper, we first analyzed the requirements and challenges of constructing GVOKG. Subsequently, the architecture of GVOKG is presented, describing the contents and the associating method of different layers of elements, and analyzing the process of information acquisition of nodes and edges in the knowledge graph. Furthermore, we evaluate the implementation effect of GVOKG through experiments to validate the effectiveness and analyze its advantages over traditional VKG. Finally, the potential application areas are discussed to analyze the advantages and limitations of GVOKG. Experimental analysis demonstrates that GVOKG can facilitate the fusion expression and comprehensive analysis of video objects and geospatial information, assisting users in understanding multi-camera video information.