VSAM IFD Web Presentation

Under the three-year Video Surveillance and Monitoring (VSAM) project [1997-1999], the Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation developed a suite of video understanding technologies for autonomous video surveillance. These algorithms automatically "parse" people and vehicles from raw video, determine their geolocations, and insert them into a dynamic scene visualization. A prototype, end-to-end surveillance system consisting of a network of active video sensors has been constructed. Within this testbed, multiple sensors cooperate to provide continuous coverage of people and vehicles moving throughout a cluttered urban environment. This web presentation presents an overview of the testbed system and automated surveillance technologies. More details can be found in a set of published papers.

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IFD Testbed System
A variety of SPUs have been incorporated into the VSAM IFD testbed system.
NVESD's Islander aircraft provides an airborne SPU platform.
Operator Control Room located in PRB on the CMU campus.
Single-Camera Surveillance
Hybrid detection algorithm (adaptive background subtraction and three-frame differencing) for moving object detection.
Object detection using temporally layered adaptive background subtraction.
Object detection from a rotating camera by perspective alignment with a collection of reference images.
Tracking people and vehicles from Wean. Active tracking keeps a person within the field of view.
Tracking objects from Smith cam and display of trajectory trails.
One minute in the life of Smith cam.
Multi-Camera Surveillance
Two sensors cooperate to actively track a vehicle through a cluttered environment.
Example of multi-camera slaving -- tracking a person.
Example of multi-camera slaving -- tracking a vehicle.
Airborne Surveillance
NVESD's Islander aircraft.
NVESD air support "bread truck" and receiving dish.
Tracking from airborne SPU using real-time image stabilization.
Acquisition of a reference mosaic and its use in sensor fixation on a geodetic scene point.
Footprints of airborne sensor being autonomously multi-tasked between three geodetic scene coordinates.
Site Models and Geolocatiion
CMU campus model (CTDB).
Geolocation by intersecting viewing rays with the terrain.
Geolocation to determine a vehicle's trajectory.
Human-Computer Interface
Using the GUI to set a region of interest (ROI) in the scene.
Three soldiers. Insertion into ModSAF.
Raju leaves town. Insertion into ModStealth.
Thermal 1. Insertion into ModStealth.
Thermal 2. Insertion into ModStealth.
Thermal 3. Insertion into ModStealth.
Thermal 4. Insertion into ModStealth.

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Acknowledgments: The VSAM IFD team would like to thank the U.S. Army Night Vision and Electronic Sensors Directorate Lab at Davison Airfield, Ft. Belvoir, Virginia for their help with the airborne operations. We would also like to thank Chris Kearns and Andrew Fowles for their assistance at the Fort Benning MOUT site, and Steve Haes and Joe Findley at BDM/TEC for their help with the CTDB site model and distributed simulation visualization software.

How this presentation was created: The text/html for this presentation was created using Netscape Composer. Movies were edited using Asymmetrix Digital Video Producer version 4.0, with occasional use of VideoMach (shareware) to crop clips and adjust their brightness/contrast/gamma.