According to a recent IHS Market Video Surveillance Installed Base Report, close to 85 million cameras will be installed in North America alone by 2021. It’s unrealistic to expect security personnel to monitor and manually search through this vast amount of video. Artificial intelligence (AI) presents a perfect solution to compensate for unmanned environments or those with limited staffing, or the loss of vigilance after looking at a screen too long.

AI can help us not only watch continuously, but also feed systems that are able to sort, organize and categorize massive amounts of data in a way that human operators cannot. And it can do so far more reliably than traditional video analytics ever did.

Previously, traditional motion analytics, depending on the environment in which they are used, could easily generate false positives since they are pixel-based. For example, if a bag is left in a train station, and an operator is doing forensic research to find out when the bag was moved, a pixel-based motion analytic can’t discern the difference between the item physically being removed or if a person is standing in front of the bag and blocking it from the camera’s view.

Likewise, real-time alerts from video security systems provide an excellent line of defense, but the susceptibility to generate false alarms from trees blowing in the wind, shadows and animals, have caused many organizations to be hesitant on all but the most guaranteed scenarios. False positive alerts can be costly for a company.

We have lived with technology in the previous decade that was not 100 percent accurate, and instead of losing credibility within the organization, many security teams have opted for the safer route, which was to avoid false alarms at all costs. The challenge now is to demonstrate to end users and the entire industry that the technology has improved so significantly, thanks to machine and deep learning, that it is reliable and can be trusted.

The most common applications today for AI-powered cameras are the same analytics that have traditionally been employed, such as loitering, intruder detection or entering/exiting an area. AI becomes a powerhouse when used to eliminate false positives from shadows, foliage or animals, by only triggering the analytics when the correct type of object is detected, such as a person or vehicle. Furthermore, a deeply integrated AI solution allows for additional metadata search parameters to speed forensic investigations, by entering search criteria such as clothing color or if subject had a bag, glasses or hat.

Forensic Post-Event Search

AI-based cameras can capture additional descriptive metadata about objects within each frame. And because the metadata is small, it adds very little to the overall bandwidth and storage requirements. Several defining characteristics of a detected object can be captured such as the color of a person’s shirt and pants, length of garment, hat or no hat, glasses or not, handbag or not, and approximate age and gender. The impact on forensic search is profound.

Imagine the time it takes to search through 10 hours of video looking for a man with a blue shirt and shorts. With the embedded metadata provided by an AI-based camera, a search yields results within seconds. AI technology can even extract clues to behaviors like falling down or fighting by using human skeletal characteristics to classify how people are positioned.

Real-Time Event Notification

By utilizing AI to detect and identify specific object types like people or vehicles, we can greatly reduce false alarms, while ignoring things like wind, rain, shadows and an errant plastic bag floating by. AI enables an entire new class of analytics, with more sophisticated logic and customization for precisely what an end user requires. For example, ignore all cars, but alert when a person comes to the door. AI can also help us count objects like people or cars more precisely.

This includes the ability to count objects accurately even when they partly “occlude” or pass in front of each other. This is key since it allows for use cases like people counting from more sensible camera view angles. This is far beyond today’s video analytics, which require a top-down view to avoid occlusion, and which gives a less useful camera view when you want to see faces.

AI represents a revolution in analytics accuracy and flexibility. Security systems will evolve to become data gathering sensors that gather actionable intelligence for not just security, but for business operations as well.