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How AI For Security Cameras Is Accelerating Business Improvements

by Staff GBAF Publications Ltd
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By Daniël Reichman, CEO and Chief Scientist for Ai-RGUS 

As the world becomes more digital and populations increase, the amount of data generated on a daily basis is staggering and only increasing. One source of data that pervades today’s society are video surveillance technologies such as security cameras. These are used for evidence as further protection against unfortunate circumstances. Recorded video is also used as evidence for further protection against unfortunate circumstances. However, when a camera system goes down, the ramifications can put public safety in jeopardy. For this reason, organizations have internal policies or regulations they must comply with, including video clarity and records retention. 

To solve some of the laborious challenges associated with the large amount of data produced by security camera systems, companies using AI software are entering the fray. The intent is to maximize safety and efficiency for all those who are involved. Here are some industry trends shaping the outlook of camera security functionalities leading to stronger business practices and processes.

Current Capabilities of AI for Security Cameras

AI pattern recognition has played an integral role in business applications. These types of applications include fraud detection, high-frequency trading, and recommendations on social media. Each of these tasks benefit from the AI software’s ability to pick out patterns from vast amounts of data. There being so much data makes it impossible for humans to go through it to accomplish these tasks. In this same way, AI can be used for pattern recognition to monitor camera systems.

AI software that can monitor security cameras is paving the way toward maximizing efficiency and safety for people and organizations. Take, for example, a university campus or a sports arena: there may be hundreds or thousands of cameras that must be maintained at a given time. The maintenance of a system of that size is almost impossible for a team of humans to monitor alone. The AI’s analysis is determining if the image that the camera is producing has blur, block, tilt, glare, or low-light. The goal with using AI to verify that each camera is indeed filming and displaying the correct image is to ensure that the video is of highest quality if it needs to be used as evidence later on. Finding such issues in a timely manner simply needs the assistance of AI technology.

For example, with indoor cameras, the camera security software can alert the user of issues relating to the infrared functionality, which is used to capture video footage in the night-time. Even a small speck of dust, a scratch on the lens, Even one strand of a spiderweb causes the infrared light to diffuse and show a very blurry image. Video recording technology for surveillance cameras is vastly improving, but it’s the role of AI to make sure that the quality of the videos being recorded has the clarity and correctness you expect.

Overcoming the Current Limitations of Security Camera Software 

AI makes predictions based on pattern recognition, however, it only recognizes very specific patterns. The patterns it recognizes can be—for example—to identify cars in video surveillance imagery. The AI learns to recognize patterns based on examples of cars in video surveillance that a person would prepare for the AI software to learn from. In this way, the AI will be able to identify cars in new images it has not seen before. There are certain assumptions that people make about AI being a magical application or an intrusive component. Namely, because it can find cars it can necessarily recognize other objects or phenomena. 

The reality is that AI technology isn’t quite capable of that. Very often, AI is deployed in a hybrid way where it feeds clips of salient information to human operators for further review. Users of camera security systems may also tend to believe that a single solution will solve all their problems. The reality is AI technology isn’t quite capable of that. Very often, AI is deployed in a hybrid way where it feeds clips of salient information to human operators for further review. AI tech developers and business leaders installing surveillance devices ultimately need to collaborate and find the right blueprint, as there is no one-size-fits-all solution yet.

There are individuals who are hesitant about surveillance security devices, believing them to be an intrusive invasion of public privacy. Privacy concerns are understandable. But the primary role of a security camera is to protect the public, goods, and infrastructure in an area where many people may be vulnerable: Be it a sports stadium or a local restaurant, public safety is at the heart of camera surveillance. 

The Road Ahead for Security Cameras

The more that business leaders come to realize the correct application for AI software, the more effectively these solutions will be trained and deployed. Additionally, if there is a symbiotic relationship between AI technician and surveillance camera operators, improving the capabilities of AI software becomes the natural outcome. This will better propel the industry by new leaps and bounds. Businesses will waste less time on tasks that AI can solve, and AI developers will be able to hone their craft to deliver the most efficient devices to the market.

Author Bio

Dr. Daniel Reichman is the CEO and Chief Scientist of Ai-RGUS, an AI startup spun out of Duke University. After receiving his Ph.D. at age 25, Dr. Reichman founded Ai-RGUS. With over 24 university publications, Dr. Reichman obtained his doctorate in Electrical and Computer Engineering from Duke University from a program fully funded by the U.S. Army Research Office. He also successfully completed the first two actuarial exams and obtained a minor in Mathematics.