Three-Dimensionalizing Surveillance Networks

James Elder, Project Leader

York University

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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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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

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Example (real, but anonymous public institution)

400 cameras 8 monitors 2 vigilant undergraduates

What if something happens?

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What if something happens?

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A better way

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Street level

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Street Level

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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 3D scene dynamics– Human and pedestrian traffic

• Integrated reporting and 3D visualization– For efficient human interpretation

• Integration into distributed software architecture– CAE S-Mission architecture

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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?

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Applications

• Public and private security

• Urban planning

• Business analytics

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Jim Little

UBC

Academic Team

YorkJames Elder Gunho Sohn

Ayman Habib

Calgary

Dave Clausi John Zelek

Waterloo

Claire Samson

Carleton

Frank Ferrie

McGill

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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

Partners: 3D Modeling and Mapping

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City of Toronto Survey & Mapping Services

• 2D and 3D mapping and modeling

• Asset management

• Bylaw enforcement

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Defence Research & Development Canada

• 3D automatic target detection & recognition

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CAE

• 3D modeling and simulation

• 3D immersive visualization

• Distributed real-time systems

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Presagis

• COTS 3D modeling and simulation products

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Applanix

• Mobile mapping and positioning– GPS + Inertial + Visual

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Array Systems

• 3D LIDAR scanning and modeling

• Scaleable signal processing systems

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dmti Spatial

• Location-based data and services

Partners: Scene Dynamics

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Ministry of Transport Ontario

• COMPASS Highway Surveillance Network

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Honeywell Video Systems

• Intelligent visual systems for surveillance and business analytics– People and object tracking

– Face detection

– Crowd density measurements

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Aimetis

• Intelligent video surveillance systems– Infrastructure

– Transportation

– Retail

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Miovision

• Automated traffic flow analysis

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Aeryon Labs

• Small electric UAVs for visual surveillance

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For more on the project…

• Kick-off workshop Saturday 9-5 in King George Room: feel free to drop in.