Mr. Coen J. van Leeuwen Profile

Coen J. van Leeuwen

at TNO Defence Security and Safety

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Author

Publications (3)

Proceedings Article | 24 October 2016 Presentation + Paper

Large scale track analysis for wide area motion imagery surveillance

C. van Leeuwen, J. van Huis, J. Baan

Proceedings Volume 9995, 99950J (2016) https://doi.org/10.1117/12.2241748

KEYWORDS: Surveillance, Sensors, Image storage, Video, Cameras, Image resolution, Inspection, Data storage, Image retrieval, Video surveillance

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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.

Proceedings Article | 6 June 2013 Paper

Recognition and localization of relevant human behavior in videos

Henri Bouma, Gertjan Burghouts, Leo de Penning, Patrick Hanckmann, Johan-Martijn ten Hove, Sanne Korzec, Maarten Kruithof, Sander Landsmeer, Coen van Leeuwen, Sebastiaan van den Broek, Arvid Halma, Richard den Hollander, Klamer Schutte

Proceedings Volume 8711, 87110B (2013) https://doi.org/10.1117/12.2015877

KEYWORDS: Video, Video surveillance, Visualization, Eye, Surveillance, Sensors, Detection and tracking algorithms, Optical tracking, Binary data, Artificial intelligence

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Proceedings Article | 23 May 2013 Paper

Anomalous human behavior detection: an adaptive approach

Coen van Leeuwen, Arvid Halma, Klamer Schutte

Proceedings Volume 8745, 874519 (2013) https://doi.org/10.1117/12.2015678

KEYWORDS: Video, Neodymium, Eye, Detection and tracking algorithms, Data modeling, Knowledge discovery, Feature extraction, Clouds, Distance measurement, Mahalanobis distance

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