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Today, it seems that threats are everywhere. We talk about the big events, like terrorist attacks and shootings that might happen anywhere and at any time, and those are valid concerns for any space that attracts people. How can we keep people safe from a threat we cannot predict? That’s a question authorities struggle with all over the world.
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Yet, as terrifying as they are, shootings and terrorism are relatively rare occurrences, and not the only problem we need to talk about. A more common issue for businesses, cities, campuses and individuals lies with the everyday crimes, theft, assault and vandalism that happens every day. In the United States alone, retailers lose nearly $50 billion a year to shoplifting and fraud.
So, what if a technology already exists that can spot potential problems in real time, ranging from suspicious behavior to precursor activities or dry-runs before an event occurs?
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Eyes in the sky
Imagine there were cameras trained on every inch of ground on a school campus or a shopping mall. Now, try to picture the number of security guards required to watch every screen, every minute of every day. It would be impossible. Yet, traditional security surveillance systems are even less practical.
In my research, I discovered iCetana, which is the kind of exciting software that inspires sci-fi movies and TV shows. Using a network of cameras, this software analyzes patterns of motion to identify anomalies. The system learns as it works, identifying patterns in human activity, and alerting guards to unusual activity such as people or vehicles exhibiting behavior different from that of established norms.
For example, an indoor stadium might host very different crowds at different times. One week, there might be a professional basketball game on the stadium floor. The next, the venue might host an aging rocker like Billy Joel, a cheerleading competition, or Disney on Ice.
Crowds at each event would behave differently, of course; but within any one crowd, there might be anomalous behavior. iCetana’s AI learning component helps the software understand what is normal for this situation.
“Normal” behavior is not predefined by set parameters, either; that would be limited to the imagination of the people writing the rules. Instead, the AI software adds to its body of knowledge minute by minute and flags any behavior that doesn’t fit the circumstances being scrutinized.
What might aberrant behavior comprise? It might be as subtle as a person moving irregularly in the middle of a crowd. This behavior would not be noticed for quite a while by human eyes, but a computer could spot it as it happened and alert security. The system could also spot someone moving toward an unauthorized location, looking around nervously or even making an effort to hide from in-view cameras.
As human system operators rule out false positives among the footage, the system is adding to its own knowledge, learning to recognize ordinary things that seem out of the ordinary, like a mother chasing her toddler making a break for an ice cream cart, or teenagers throwing snowballs in the park. Increasingly, this learning minimizes red flags, leaving only events that really matter for operators to decide how to respond.
Is this an invasion of privacy?
Detractors argue that this level of surveillance is an invasion of privacy. Realistically, though, that ship sailed long ago. Cameras already exist. In Europe, a video system called CCTV records almost every inch of public space. Office buildings, banks, retailers, warehouses, hotels, police department, and individual homeowners all use video cameras.
Video footage is regularly used to solve crimes, but its effectiveness in preventing crime remains unclear.
What’s important here for entrepreneurs to know? What’s important is that this software offers proactive insights. It can help get security on the spot while the crime is taking place . . . or even before it happens.
In Boston, after two bombs detonated at the 2013 Marathon, leading to three deaths and 264 injuries, life-changing CCTV footage helped police identify and track the Tsarnaev brothers. Hundreds of videos, thousands of photos and phone records were analyzed by hand. Without that surveillance, the brothers might have simply vanished — and committed more horrendous acts.
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While it’s helpful to identify criminals after the fact, what if a system could see a person acting unusually in a crowd? Or identify a person in a dark parking lot, circling around to the back door of a theater or a dance club?
How many tragic events could be thwarted before they happen, by police or security guards already on scene? And how many retailers and other entrepreneurs could be spared expensive financial losses?