Volume II-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 29-34, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-29-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 29-34, 2015
https://doi.org/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

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 C. Wu et al.
  • 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.