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BRS Labs: Harris Poll Shows 96 Percent of Americans Support Video Surveillance to Counteract Terrorism
Source: BRS Labs 2009/10/27

Current camera surveillance, however, is largely ineffective, primarily used after an event, and subject to the attention span of security personnel monitoring multiple screensCurrent camera surveillance, however, is largely ineffective, primarily used after an event, and subject to the attention span of security personnel monitoring multiple screens

A recent Harris Poll survey indicates that 96 percent of US citizens feel the federal government and law enforcement agencies should be able to use video surveillance in an effort to counteract terrorism and help protect US citizens in specific public places. Four out of five adults feel that in extreme cases, such as a terrorist attack, the government should be able to use any available means to protect citizens, and more than half (54 percent) of US adults are even willing to put a portion of the government's stimulus funds toward setting up video surveillance to help reduce crime.

The results are at odds with current perceptions about the use of video surveillance, by revealing that only a small minority of Americans is concerned about the federal government or law enforcement agencies using surveillance cameras to monitor public places. That Americans don't mind being watched is especially relevant in light of the recently exposed domestic terror plot in Boston, and subsequent FBI intelligence indicating that al-Qaida recruits are reportedly being encouraged to perform acts of terrorism inside the U.S.

However, citizen support of video surveillance rests on the assumption that more cameras will result in more secure environments, but that isn't the case. Recently, the security staff at the George Washington Bridge in New York City — responsible for monitoring bridge cameras and security kiosks — was photographed sleeping on the job. Thus, camera proliferation alone will not solve the problem. Many of these cameras go completely unmonitored because there are simply not enough human eyes available to watch all of the video feeds.

“The widespread adoption of video-camera technology has not made the job of the security officer any easier, nor has it helped obtain actionable intelligence before an intrusion,” said John Frazzini, President of BRS Labs and a former Secret Service agent.

“We have been working with high-level security customers in the U.S. and around the world to put a new approach to work-behavioral analytics,” Frazzini said. “Ten days after the terrorist attacks in Mumbai last Thanksgiving, a major international hotel installed BRS Labs' software, AISight, which was designed to autonomously monitor hundreds of cameras simultaneously, and to provide real-time actionable intelligence. In just a few days the hotel's security staff was able to improve the safety of the hotel's perimeter. We are also deployed in several high-security US locations including seaports, power plants, nuclear plants, and global financial institutions.”

BRS Labs' technology blends computer vision, machine learning and artificial intelligence; it sends instant and reliable alerts to a myriad of PDA devices, and the software is compatible with all legacy camera systems.

“Traditional video surveillance approaches have failed because they ignore the fact that every environment is unique,” said Ray Davis, founder of BRS Labs. “These methods also require expensive, labor-intensive programming to define specific objects or activities a system should look for, so unexpected security incidents are missed. Any new technology approach to video surveillance must deliver the right level of protection and the right level of privacy from small, simple deployments to the most complex security environments without human intervention required.”

AiSight takes visual input from a camera, learns what activities and behaviors are typical, and generates real-time alerts when it identifies activities that are not normal. It is a reasoning-based surveillance technology that functions in a manner similar to the human brain. It takes in external visual input (computer vision), while its machine learning engine observes the scene, learns and recognizes behavioral patterns and responds accordingly. Surveillance is 24-7, and since the software learns the scene, the false positives are greatly reduced.

The Harris organization's online survey, commissioned by BRS Labs, was conducted from May 28 through June 1, 2009, with 2,416 adults (ages 18 and over) in the U.S. interviewed.

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