The distributed eye

Dec. 1, 2007
Surveillance system captures events in real time in an integrated, georeferenced 3-D model.

Surveillance systems based on distributed camera networks are emerging as demand for enhanced safety has increased, including monitoring of power plants, shopping malls, hotels, museums, borders, airports, railroad stations, and parking garages. These installations often have a large number of cameras that are widespread and hard to watch.

In typical CCTV surveillance systems, the live video streams of potentially hundreds of cameras are centrally recorded and displayed on a huge monitor wall. Security personnel then have to analyze the streams and detect and respond to situations. For example, in a typical Las Vegas casino, approximately 1700 cameras are installed. To track a suspect, the person must be followed by a camera and when the person leaves one camera’s view, there must be a switch to the next appropriate camera to continue tracking.

Moreover, suspicious activity has to be detected visually by security personnel. One military study indicates that after approximately 22 minutes, an operator will miss up to 95% of activity in a scene. Privacy concerns and personnel costs are other major issues.

A smarter eye

To address these limitations, Sven Fleck and colleagues from the University of Tübingen (Tübingen, Germany) developed the SmartSurv surveillance system and have recently created a company to explore commercialization options: SmartSurv Vision Systems (Tübingen, Germany).

The SmartSurv system is a distributed, smart-sensor-based system that reflects events in real time in an integrated, georeferenced 3-D model that is independent of camera views. The company was founded in collaboration with camera-maker Matrix Vision (Oppenweiler, Germany) and the University of Tübingen.

The system consists of the SmartTrack Camera Network-a distributed network of smart cameras from Matrix Vision capable of tracking in real time; SmartViz Visualization System-a system capable of visualization within a consistent, integrated, and georeferenced model that uses Google Earth; and Server Node-a node that collects all data from the camera network and provides the interface to each visualization node. The results of the surveillance system are permanently updated on a Web server that can be accessed by multiple users.

The SmartTrack network of camera nodes is capable of video analysis in real time; only the results are transmitted on a higher level of abstraction.

In the SmartSurv system, what algorithmically belongs to the camera is also physically performed in the camera, resulting in various benefits. As the entire real-time video analysis is embedded inside each smart camera node, the video analysis can work on the raw, uncompressed, and thus artifact-free sensor data and transmit only the results-which requires very limited bandwidth and makes the use of Ethernet possible. The potential number of cameras could reach into the thousands.


SmartSurv also provides an intuitive visualization system that can integrate the results of the whole sensor network in one consistent and georeferenced model based on Google Earth. Instead of showing raw live video streams, only the relevant information is presented in one of the available visualization options.

All video analysis results of all objects within a SmartSurv camera network installation can be seen in real time, and each object is visualized either as a live image or as a stylized icon for increased privacy. The viewpoint within Google Earth can be chosen during runtime, independent of the objects being tracked and independent of the camera nodes.

A second visualization option is based on 3-D models acquired by a mobile platform containing a laser scanner and panoramic camera, which is the subject of current research at the University of Tübingen. Besides the SmartViz interface in this option, the results of the SmartTrack network can be integrated in any action chain, such as sending alarms to mobile phones, security companies, or the police.

Naturally, the SmartSurv team is looking at many other applications for which the system could be commercialized, including tracking shopping patterns, observing properties, and monitoring the elderly.

About the Author

Conard Holton | Editor at Large

Conard Holton has 25 years of science and technology editing and writing experience. He was formerly a staff member and consultant for government agencies such as the New York State Energy Research and Development Authority and the International Atomic Energy Agency, and engineering companies such as Bechtel. He joined Laser Focus World in 1997 as senior editor, becoming editor in chief of WDM Solutions, which he founded in 1999. In 2003 he joined Vision Systems Design as editor in chief, while continuing as contributing editor at Laser Focus World. Conard became editor in chief of Laser Focus World in August 2011, a role in which he served through August 2018. He then served as Editor at Large for Laser Focus World and Co-Chair of the Lasers & Photonics Marketplace Seminar from August 2018 through January 2022. He received his B.A. from the University of Pennsylvania, with additional studies at the Colorado School of Mines and Medill School of Journalism at Northwestern University.

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