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Large scale track analysis for wide area motion imagery surveillance

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Author: Leeuwen, C.J. van · Huis, J.R. van · Baan, J.
Publisher: SPIE
Source:Carlysle-Davies, F.Bouma, H.Stokes, R.J.Yitzhaky, Y.Burgess, D.Owen, G., Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII. 26 September 2016 through 27 September 2016, 9995
Proceedings of SPIE - The International Society for Optical Engineering
Identifier: 745586
doi: doi:10.1117/12.2241748
ISBN: 9781510603943
Article number: 99950J
Keywords: Data Analysis · Motion in Motion · Multi-scale Images · Wide Area Motion Imagery · Artificial intelligence · Building materials · Color · Computer vision · Crime · Data mining · Data reduction · Graphical user interfaces · Image segmentation · Information analysis · Learning systems · Motion analysis · Search engines · Terrorism · Unmanned aerial vehicles (UAV) · User interfaces · Vehicles · Detection and tracking · High resolution data · High resolution image · Machine learning techniques · Maneuver detection · Motion in Motion · Multi-scale Images · Wide-area motion imageries · Big data · 2016 ICT 2015 Observation, Weapon & Protection Systems · MCS - Monitoring & Control Services II - Intelligent Imaging · TS - Technical Sciences


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. © 2016 SPIE. The Society of Photo-Optical Instrumentation Engineers (SPIE)