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An Adaptive Organization Method of Geovideo Data for Spatio-temporal Association Analysis : Volume Ii-4/W2, Issue 1 (10/07/2015)

By Wu, C.

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Book Id: WPLBN0004014005
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: An Adaptive Organization Method of Geovideo Data for Spatio-temporal Association Analysis : Volume Ii-4/W2, Issue 1 (10/07/2015)  
Author: Wu, C.
Volume: Vol. II-4/W2, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Zhou, Y., He, F., Zhang, Y. T., Wu, C., Xie, X., Zhu, Q., & Du, Z. Q. (2015). An Adaptive Organization Method of Geovideo Data for Spatio-temporal Association Analysis : Volume Ii-4/W2, Issue 1 (10/07/2015). Retrieved from

Description: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, P. R. China. Public security incidents have been increasingly challenging to address with their new features, including large-scale mobility, multi-stage dynamic evolution, spatio-temporal concurrency and uncertainty in the complex urban environment, which require spatio-temporal association analysis among multiple regional video data for global cognition. However, the existing video data organizational methods that view video as a property of the spatial object or position in space dissever the spatio-temporal relationship of scattered video shots captured from multiple video channels, limit the query functions on interactive retrieval between a camera and its video clips and hinder the comprehensive management of event-related scattered video shots. GeoVideo, which maps video frames onto a geographic space, is a new approach to represent the geographic world, promote security monitoring in a spatial perspective and provide a highly feasible solution to this problem. This paper analyzes the large-scale personnel mobility in public safety events and proposes a multi-level, event-related organization method with massive GeoVideo data by spatio-temporal trajectory. This paper designs a unified object identify(ID) structure to implicitly store the spatio-temporal relationship of scattered video clips and support the distributed storage management of massive cases. Finally, the validity and feasibility of this method are demonstrated through suspect tracking experiments.



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