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January 2005

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Automating Camera Surveillance

By Andrew Kalukin
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W ith practice, you can recognize the video spies in the city of Washington, DC. To a casual observer, they resemble lampposts. Some of the cameras have a 360 degree view and magnify by a factor of 10-17. Some are equipped with night vision and can zoom in on a target well enough to read text on a written page or look into a building. Most are placed at locations that would not come to mind as primary terrorist targets: Smithsonian Castle, the U.S. Department of Labor, Dupont Circle, Union Station, Wisconsin Avenue, the Old Post Office, and the Banana Republic in Georgetown. 

Though the targets they view may not stand out as particularly vulnerable to terrorism, the cameras are placed strategically for the purpose of monitoring demonstrations and protests. 

One of the first occasions for their use was a demonstration in April 2000 against the World Bank and International Monetary Fund (IMF). Supplemental data from the U.S. park police monitoring demonstrations by helicopter was sent as a digital feed to the Metropolitan Police Department. The DC police, the FBI, the Secret Service, and the DC school system agreed to pool data as needed. Though the police have stated there are only a dozen cameras, these cameras can link to about 1,000 other government cameras to make up a network such as might be found in a NASA or defense command center. 

Similar systems exist in other cities for the same purpose. During antiwar protests in Boston at the 2004 Democratic National Convention, the police informed the media that camera systems would be used to guard against acts of terrorism. According to ANSWER (Act Now to Stop War and End Racism), photographs of protesters from past marches were circulated to bus drivers and other mass transit employees to train them to recognize “terrorists.” In Manhattan, a person walking on a street is in view of at least one of 2,400 cameras. 

In reaction, privacy advocates who view these surveillance measures with alarm have begun to publish the locations of surveillance devices in major U.S. cities, to allow others who object to the technology to chart surveillance-free paths through the streets. 

The privacy issues surrounding Closed Circuit TV (CCTV) become more complicated when computer vision technology is applied to surveillance. A controversy resulted in 2001 when authorities used face recognition technology and CCTV at the Tampa Bay Super Bowl to search for criminals and terrorists. The action led to 19 arrests, all of them for petty crimes, with no record of whether these arrests were legitimate. The ensuing public furor led several legislators, such as Dick Armey, to propose laws for protecting privacy and regulating the use of biometric technology. 

When the results of video searches are combined with other existing databases, powerful methods of identification and tracking become possible. Unique body marks make identification much easier. In Fort Worth, Texas police can track gang members by applying a software package called GangNet. By typing a description of tattoos into the database, the software can produce pictures of members wearing those tattoos. Similar searches can be performed on nicknames, vehicle numbers, telephone numbers, or partial license plate numbers. Salinas, California received federal funding for a Geographic Information System to carry out crime tracking of gangs. In Manalapan, Florida—one of the nation’s wealthiest cities—cameras and computers have been set up to run background checks on every car and driver that enters. The system alerts a 911 dispatcher if the car is stolen or the driver is suspected of a crime. Infrared cameras record each car’s license number and other cameras photograph the driver. 

In 2003, Ohio transportation officials began testing the use of unpiloted aircraft equipped with video, infrared cameras, and other sensors to monitor traffic jams. The information from aerial monitoring is intended to help police looking for the best route to an accident scene, as well as assist traffic planners, emergency workers, truck companies, and commuters. Some of the planes—drones—are as small as a model aircraft. The military can use these planes to send back real-time images of battle to commanders. In November 2003, the CIA used a drone to fire a missile into a car containing six alleged al-Qaida members. Unpiloted aerial vehicles—UAVs—have attacked high-priority targets in Afghanistan and Iraq. In December 2002, Senate Armed Services Committee Chair John Warner (R-VA) indicated interest in using drones for homeland security. In January 2003, as a cost-saving measure, a U.S. Congressional Research Service report suggested replacing piloted fighters flying combat air patrols (CAP) over U.S. cities with UAVs armed with air-to-air missiles. It is unclear whether the FAA would have authority over the UAVs in such a program. Furthermore, the UAVs may be too small to be seen and fly too low to be detected by radar. (The possible use of UAVs to deliver biological and chemical attacks has been a concern of the federal government.) Though the cameras used in these UAVs have been remotely piloted vehicles (RPVs) unequipped with artificial intelligence processing, efforts to develop robotic autonomous vehicles are being funded by DARPA (Defense Advanced Research Projects Agency).

Military and local law enforcement agencies already use video surveillance to automate threat response. In Broward County, Florida, Port Everglades selected ObjectVideo VEW software to protect its perimeter. The software contains a tripwire feature that allows security personnel to create virtual perimeters on land and water by drawing a box on a digital view of what the camera is observing. Unknown people or vehicles crossing the tripwire boundaries signal an alert. 

Archival video data are vulnerable to the same trends in industry and government that have led to information being sold as a commodity. Information about individuals is sold and traded routinely for marketing, charity solicitations, and political polling. Individuals may find more difficulty controlling the distribution of archived surveillance imagery, where the data are more likely to be collected surreptitiously. The breakdown of privacy in the trade of personal information already makes it possible for government agencies such as the FBI to bypass the government ban against information collection for people who are not suspects of investigation by simply accessing personal information that is already commercially available. 

Some of the controversies of video surveillance came to public attention during the Congressional discussion of the proposed Total Information Awareness (TIA) research programs of the Pentagon. Several research programs in TIA exploited video pattern recognition. HumanID included research projects to recognize humans from a distance based on face and gait along with other biometric tools. Though public outcry caused the TIA budget to be canceled by Congress in September 2003, some of the programs continued under other cover, such as Novel Intelligence from Massive Data (NIMD), Non-Obvious Relationship Awareness (NORA), Adaptive Concept Understanding from Modeled Enterprise Networks (ACUMEN), Computer-Assisted Passenger Prescreening System (CAPPS II), and Multi-state Anti-Terrorism Information Exchange (MATRIX). 

Among the pattern matching efforts was a project known as Video Analysis and Content Exploitation (VACE). The goal of VACE was automatic content detection and recognition in “video scenes of various indoor and outdoor activities involving people, meetings, and vehicles, and TV news broadcasts,” according to the Advanced Research and Development Activity (ARDA) website. Research goals included recognition of people, event detection and understanding, video query, multi-modal video data mining, and object identification from motion. The VACE solicitations have closed, but as of December 2003, plans for workshops in VACE and other programs were still planned for 2004. 

Another military project for widespread video collection was offered as a DARPA BAA solicitation in May 2003—Combat zones That See (CTS). The goal of CTS is to develop video understanding of multiple data feeds arriving from many sources to support military operations in urban terrain. The military is interested in tracking vehicles moving from one camera location to another. 

The ability to extract information from video images is so widely sought in the scientific community that the complete discontinuation of funding for similar programs seems unimaginable. The National Geo- spatial-Intelligence Agency has plans to post solicitations for geo- spatial information visualization and to award $2.5 million in FY05 and FY06. Military applications of pattern recognition and video data mining continue to be developed by means of Department of Defense solicitations to contractors in the form of BAA, SBIR, and STTR awards. These programs include efforts at automating algorithms for detecting human intentions in subjects appearing in video films, for distinguishing decoys from targets, and for synchronizing many UAVs to carry out simultaneous reconnaissance and attack. It is possible that in the future, covert or privatized TIA-like programs may escape the scrutiny of Congress. It also seems certain that computer vision will find increasing applications in autonomous vehicles and that efforts will be made to explore the  abilities of robotic devices to carry out automated warfare. 

Though video surveillance is a passive activity, the data mining of video records to profile individuals is surreptitiously invasive. Given the difficulty of detecting video surveillance systems hidden on the ground or in the air, it would be difficult to enforce restraints against misuse of such data by either private agents or governments. Imagine each city street patrolled by cameras, some fixed, some moving, equipped to detect a variety of crimes ranging from minor traffic violations to terrorism. A few observation stations pass their data to human observers and the rest function automatically, assessing complex threats to law and order with the acuity of trained professionals. UAVs fly overhead in networked configurations called swarms, passing information to one another that enables the group as a whole to respond to emergency situations with greater organization than an individual platform could achieve. Most of the cameras and UAVs would be invisible to casual observers. Though the components of surveillance equipment and software are cheap, the infrastructure to support exchanges of information across many databases and networks could be afforded only by large corporations or government institutions. This implies a poten- tially asymmetrical situation in which surveillance becomes a weapon of class warfare. 

The ultimate realization of a surveillance society would be dictatorial and intolerant of dissent. Total control would not require a lone autocrat—a complex network of private agencies could control the surveillance devices with little or no accountability. Hierarchies of privilege favoring race or class are built implicitly into existing surveillance systems—for example, homeless residents of a city are more often targeted by video surveillance systems than are other citizens. Practices that are too blatantly oppressive to succeed in the U.S. or European countries could be exported to dictatorships abroad, to nations that can be exploited for their resources and brought under the hegemony of global corporate powers. The distinction between the benign possibility and its terrifying alternative depends on who controls the data. If data collection is transparent and access to the data is egalitarian, the most beneficial potential of the technology can be realized. If special political groups, religious factions, or corporate interests attain control over the use of data—historically, the most probable scenario—those groups could enforce unprecedented social control. 

Recent developments in computer vision, robotics, and pattern matching increase the possibility of drastic social transformations. The dictatorship of Big Brother had one small limitation of power: it depended on the obedience and vigilance of subordinates to enforce control. The application of data mining methods to massive video data sets enables a sufficiently organized power to outmatch humans in carrying out surveillance. Though the robot soldier and the robot police are not yet reality, present technological achievements can lead to this future possibility. In case these apprehensions seem too dire, it is worth remembering how easily other invasions of privacy such as drug testing have come to be accepted generally, even when they require active awareness by participants. Polls show that people are often willing to give up some privacy in exchange for the perception of better security. Fears of terrorism, appeals to patriotism, economic incentives, and the insidiousness of visual surveillance prevent many people from questioning misuses of similar technology—especially when governments and corpora- tions shroud their research and development.  

Andrew Kalukin’s signal and image processing  research has been published in scientific journals and used by government agencies and private industry. Photos are courtesy of www.observingsurveillance.org. 
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