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Three-Dimensionalizing Surveillance Networks

Three-Dimensionalizing Surveillance Networks. James Elder, Project Leader York University. The challenge. To use persistent visual surveillance data to maintain and improve the security and efficiency of our urban centres in the face of rapid growth and increasing complexity.

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Three-Dimensionalizing Surveillance Networks

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  1. Three-Dimensionalizing Surveillance Networks James Elder, Project Leader York University

  2. The challenge • To use persistent visual surveillance data to maintain and improve the security and efficiency of our urban centres in the face of rapid growth and increasing complexity

  3. Current obstacles • Most surveillance data are ignored due to lack of adequate manpower and reliable visual algorithms. • Persistent visual surveillance systems are poorly integrated with other forms of geospatial information • Surveillance cams compress the 3D scene into 2D, making inference difficult

  4. 8 monitors 2 vigilant undergraduates 400 cameras Example (real, but anonymous public institution) What if something happens?

  5. What if something happens?

  6. A better way

  7. Street level

  8. Street Level

  9. Goals • Automatic, efficient, scaleable methods for extraction and integration of 2D and 3D urban data at street level • Surveillance video, UAV photogrammetry, airborne & terrestrial LIDAR… • Automatic inference of 3D scene properties • Scene segmentation, building characteristics, foliage modeling • Automatic inference of 3Dscene dynamics • Human and pedestrian traffic • Integrated reporting and 3D visualization • For efficient human interpretation • Integration into distributed software architecture • CAE S-Mission architecture

  10. Scientific Questions • There are many, e.g., • How can 3D urban scene information be reliably extracted from single-view video? • How can individuals be discriminated in crowds? • How can free-form structures (e.g., trees) be reliably segmented from the scene? • How can multiple forms of geolocation data (GPS, inertial, visual) be integrated to optimize positioning?

  11. Applications • Public and private security • Urban planning • Business analytics

  12. Claire Samson Frank Ferrie Carleton McGill Jim Little Ayman Habib Calgary UBC John Zelek Dave Clausi Waterloo James Elder Gunho Sohn York Academic Team

  13. Associated Korean Land Spatialization Group Projects • Project 1. Real-time Aerial Monitoring System • Project Leader: Impyeong Lee, Head, Dept. of Geoinformatics, The University of Seoul • Project 2. Mobile Mapping at Street Level • Project Leader: Taejung Kim, Associate Professor, Dept. of Geoinformatic Engineering, Inha University

  14. Partners: 3D Modeling and Mapping

  15. City of Toronto Survey & Mapping Services • 2D and 3D mapping and modeling • Asset management • Bylaw enforcement

  16. Defence Research & Development Canada • 3D automatic target detection & recognition

  17. CAE • 3D modeling and simulation • 3D immersive visualization • Distributed real-time systems

  18. Presagis • COTS 3D modeling and simulation products

  19. Applanix • Mobile mapping and positioning • GPS + Inertial + Visual

  20. Array Systems • 3D LIDAR scanning and modeling • Scaleable signal processing systems

  21. dmti Spatial • Location-based data and services

  22. Partners: Scene Dynamics

  23. Ministry of Transport Ontario • COMPASS Highway Surveillance Network

  24. Honeywell Video Systems • Intelligent visual systems for surveillance and business analytics • People and object tracking • Face detection • Crowd density measurements

  25. Aimetis • Intelligent video surveillance systems • Infrastructure • Transportation • Retail

  26. Miovision • Automated traffic flow analysis

  27. Aeryon Labs • Small electric UAVs for visual surveillance

  28. For more on the project… • Kick-off workshop Saturday 9-5 in King George Room: feel free to drop in.

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