ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 29-34, 2015
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-4-W2/29/2015/
doi:10.5194/isprsannals-II-4-W2-29-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
10 Jul 2015
AN ADAPTIVE ORGANIZATION METHOD OF GEOVIDEO DATA FOR SPATIO-TEMPORAL ASSOCIATION ANALYSIS
C. Wu1, Q. Zhu1,2, Y. T. Zhang1, Z. Q. Du1, Y. Zhou3, X. Xie1, and F. He1 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, P. R. China
2National-local Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Running Safety, Southwest Jiaotong University, P. R. China
3School of Resource and Environment, University of Electric Science and Technology, P. R. China
Keywords: GeoVideo, Data Management, Spatio-Temporal Association Analysis, Geographic Semantic Abstract. 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.
Conference paper (PDF, 1003 KB)


Citation: Wu, C., Zhu, Q., Zhang, Y. T., Du, Z. Q., Zhou, Y., Xie, X., and He, F.: AN ADAPTIVE ORGANIZATION METHOD OF GEOVIDEO DATA FOR SPATIO-TEMPORAL ASSOCIATION ANALYSIS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 29-34, doi:10.5194/isprsannals-II-4-W2-29-2015, 2015.

BibTeX EndNote Reference Manager XML