A content-aware geographic video multi-level association method

Abstract

The invention relates to a content-aware geographic video multilayer correlation method. The method comprises the following steps: a) unifying the structural features of a multi-source geographic video; b) analyzing the common features of temporal and spatial variation during which a track object is used as a carrier, and establishing an associated element view which combines the content semantics and the geographic semantics under a uniform basis reference; c) extracting a geographic video data set which comprises temporal and spatial variation features, and establishing regular function mapping from data to associated elements; and d) distinguishing the relevancy of geographic video data examples on the basis of a rule and calculating the association distance, gathering the geographic data layer by layer according to the hierarchical property of the association, and ranking data objects in the set according to the association distance. According to the method, the global association, in which the comprehensive geographic video contents are similar, geographically related under the uniform basis reference can be supported, so that the cognitive calculation ability and information expression efficiency of multi-scale complicated behavior events in discontinuous or cross-region monitoring scenes behind multiple geographic videos in monitoring network systems can be enhanced.

Description

technical field [0001] The invention belongs to the technical field of geospatial data processing, in particular to a content-aware geographic video multi-level association method. Background technique [0002] GeoVideo is video data containing geographic spatiotemporal reference information. As a new media for space-time expression, geographic video images have the advantages of dynamic, real-time, and realistic expression of geographic space, which conforms to the characteristics of human intuitive perception and cognition, and has become an important geographic space widely used in public security incident monitoring and emergency management. type of data. Under the background of large-scale construction of surveillance network infrastructure, geographic video in the form of unstructured streaming media has included a total of PB-level or even EB-level historical archives and large-scale intensive video streams accessed in real time. , Massive, unstructured and complex ...

AI Technical Summary

Problems solved by technology

These association methods lack the semantic and contextual thinking of the video content in complex dynamic surveillance scenes in the macro-pattern of geospatial space, and it is difficult to support the semantic association required for cross-temporal issues, especially for multi-scale geographic issues under discrete spatio-temporal windows. Video content association

Since the local data corresponds to the short-term behavior of the object at a specific location, it is difficult to understand the multi-scale and complex behavior event information contained in the data

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