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Proceedings Paper

Large scale track analysis for wide area motion imagery surveillance
Author(s): C. J. van Leeuwen; J. R. van Huis; J. Baan
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Paper Abstract

Wide Area Motion Imagery (WAMI) enables image based surveillance of areas that can cover multiple square kilometers. Interpreting and analyzing information from such sources, becomes increasingly time consuming as more data is added from newly developed methods for information extraction. Captured from a moving Unmanned Aerial Vehicle (UAV), the high-resolution images allow detection and tracking of moving vehicles, but this is a highly challenging task. By using a chain of computer vision detectors and machine learning techniques, we are capable of producing high quality track information of more than 40 thousand vehicles per five minutes. When faced with such a vast number of vehicular tracks, it is useful for analysts to be able to quickly query information based on region of interest, color, maneuvers or other high-level types of information, to gain insight and find relevant activities in the flood of information.

In this paper we propose a set of tools, combined in a graphical user interface, which allows data analysts to survey vehicles in a large observed area. In order to retrieve (parts of) images from the high-resolution data, we developed a multi-scale tile-based video file format that allows to quickly obtain only a part, or a sub-sampling of the original high resolution image. By storing tiles of a still image according to a predefined order, we can quickly retrieve a particular region of the image at any relevant scale, by skipping to the correct frames and reconstructing the image. Location based queries allow a user to select tracks around a particular region of interest such as landmark, building or street. By using an integrated search engine, users can quickly select tracks that are in the vicinity of locations of interest. Another time-reducing method when searching for a particular vehicle, is to filter on color or color intensity. Automatic maneuver detection adds information to the tracks that can be used to find vehicles based on their behavior.

Paper Details

Date Published: 24 October 2016
PDF: 8 pages
Proc. SPIE 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, 99950J (24 October 2016); doi: 10.1117/12.2241748
Show Author Affiliations
C. J. van Leeuwen, TNO Technical Sciences (Netherlands)
J. R. van Huis, TNO Technical Sciences (Netherlands)
J. Baan, TNO Technical Sciences (Netherlands)


Published in SPIE Proceedings Vol. 9995:
Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII
Douglas Burgess; Gari Owen; Henri Bouma; Felicity Carlysle-Davies; Robert James Stokes; Yitzhak Yitzhaky, Editor(s)

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