Intelligent Analysis for Georeferenced Video Using Context-Based Random Graphs

Abstract EN

Video sensor networks are formed by the joining of heterogeneous sensor nodes, which is frequently reported as video of communication functionally bound to geographical locations.

Decomposition of georeferenced video stream presents the expression of video from spatial feature set.

Although it has been studied extensively, spatial relations underlying the scenario are not well understood, which are important to understand the semantics of georeferenced video and behavior of elements.

Here we propose a method of mapping georeferenced video sequences for geographical scenes and use contextual random graphs to investigate semantic knowledge of georeferenced video, leading to correlation analysis of the target motion elements in the georeferenced video stream.

We have used the connections of motion elements, both the correlation and continuity, to present a dynamic structure in time series that reveals clues to the event development of the video stream.

Furthermore, we have provided a method for the effective integration of semantic and campaign information.

Ultimately, the experimental results show that the provided method offers a better description of georeferenced video elements that cannot be achieved with existing schemes.

In addition, it offers a new way of thinking for the semantic description of the georeferenced video scenarios.