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View problems Impact 50 Percent of Surveillance Cameras Each Year

With help from artificial intelligence, executives can automate video surveillance inspections to immediately detect potential issues.

Video surveillance technology has become a tool of choice for organizations looking to protect their facilities and staff, with some estimates putting the number of installed surveillance cameras in excess of one billion worldwide. 

For the majority of these camera installations, the value lies in the recorded video, providing visual evidence of potential incidents—a shoplifter in a retail store, fraudulent activity such insider threats or a faked “slip and fall,” an attempted mugging on a city street, or a forklift accident in a warehouse. Security, legal and operations staff, and law enforcement rely on recorded video for internal investigations of security incidents, to counteract liability claims and to prosecute offenders.

Yet over the course of one year, an estimated 50% of cameras will have a view problem, either impacting the clarity of the image, a change in the field of view of the camera to look in the wrong direction, or obstructing the video that is being recorded. This is why it is important to regularly monitor the health of surveillance cameras, but this task can be impossible for a person to manage alone. With many camera installations numbering in the hundreds, thousands, or more, the job can be tedious and take hours each day for a person to accomplish. Fatigue and boredom, factors not to underestimate, affect the quality of the verification as well.

Using artificial intelligence to regularly monitor camera health is creating huge gains in this highly manual inspection process where security staff physically inspect each camera to ensure that the camera’s field of view is not askew, the lens is not dirty or blurred and that the camera has enough storage capacity to capture these potential incidents. Using new AI-powered software, this process can now be automated to immediately detect any potential camera issues without a physical inspection of the camera and alert security staff to correct the problem.

“In the same way that you would not consider stepping into a car without airbags, deploying a camera system without reasonable measures of protection that the devices will function when you need them should be a thing of the past,” said Daniel Reichman, Ph.D., chief executive officer and chief scientist of Ai-RGUS.  “Automating the camera inspection process saves operator time and provides peace of mind to the camera system owner because they can be sure the task is completed each day and that they have complete visibility into the status of the camera system they depend on.”

Originally developed and deployed at Duke University in 2017 to manage the school’s more than 2,000 cameras, software developed by Ai-RGUS monitors all cameras within a security system to alert users if the image is blurred, blocked, tilted, or otherwise faulty. The Ai-RGUS solution also automatically catches other surveillance issues, such as low light conditions, potential camera/NVR/DVR misconfigurations or failures, incorrect video timestamp, and missing or not enough days of recordings.

The solution can also support an organization’s cybersecurity program by automating critical cybersecurity updates to maintain the integrity of the camera system. Using the software, security staff can use the system to automatically execute firmware upgrades and remotely remediate an array of issues, including the ability to change out-of-date or insecure passwords, and reboot devices and cameras. 

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Ai-RGUS, Artificial Intelligence (AI), Business Resiliency, Cameras, Daniel Reichman, Duke University, Monitors, security, Surveillance, Video Surveillance Technology

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