AI-Assisted Video Monitoring: What It Is, How It's Different, and What It Brings to the Table.

10 October 2017
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Large retail stores. Shopping malls. College campuses. Transportation hubs. These are all environments that are incredibly fluid — at any given time there could be hundreds or even thousands of people walking among them. 

This makes maintaining normal operations on a good day an uphill battle, to say nothing of how difficult things can quickly become when you let the topic of site security bleed into the discussion.

In an effort to keep us safe, as well as to protect a company's assets and reputation, security officials are constantly on the lookout for better and more efficient ways to improve a site's security. There are variables that even a trained, skilled eye could miss in a naturally chaotic environment like a sports arena. With those goals in mind, AI-assisted video monitoring is something that every security-minded professional should make an effort to stay aware of for a host of different reasons.

What is AI-Assisted Video Monitoring?

AI-assisted video monitoring software uses technologies like computer vision, machine learning, advanced algorithms, and more to take protection to the next conceivable level. Once installed, these systems actually ‘learn’ and ‘remember’ what normal conditions in a given environment look like. Whenever circumstances break from that ‘normal’ definition and move into ‘abnormal’ territory, the appropriate people are notified as quickly as possible.

This means that if a person starts displaying suspicious behaviour, for example, the system would be able to both recognise that this is not normal and display the footage to an operator, who can then decide whether to take action or disregard. This also gives the system an opportunity to identify trends that you may have even been unaware of, like certain areas that seem to experience the same types of ‘problems’ time and again.

This is contrary to the more traditional rule-based surveillance software in a number of important ways. With rule-based software, your accuracy is ultimately limited to a small set of scenarios that the software is programmed to recognise. AI-assisted video monitoring, on the other hand, would compare current environmental conditions to the accepted definition of ‘normal’ in an effort to identify activities that are or are not suspicious. This also means that if something totally unexpected happens, like if a suspicious activity is taking place that is outside the boundaries of what you could have predicted yourself, you and your team will still be made aware.

The Strengths and Limitations of AI-Assisted Video Monitoring

Going beyond the technical aspect of AI-assisted video monitoring, it’s a technology that has become increasingly popular in nearly every industry over the last few years thanks to the sheer business value it can generate alone.

One of the biggest benefits of AI-assisted surveillance comes by way of dramatically reduced operator fatigue. These systems are literally getting ‘smarter’ as time goes on and as they're given the opportunity to analyse more video of what ‘normal’ conditions actually look like. This means that they actually improve in accuracy the more they're used, ultimately taking the burden off of the operators themselves and giving them the peace of mind that comes with knowing they're missing less incidents all the time.

None of this is to say that AI-assisted video monitoring is perfect, however. While it has a very high identification rate, the technology is still known to be unable to identify a small number of situations that are inherently fluid. Shoplifting is one example of a scenario that would still require a trained human eye and intuition. AI-assisted monitoring software may miss certain shoplifting events as they appear less ‘abnormal’ than others.

Many other types of situations do fall into the real-time detection capabilities of AI-assisted monitoring, however. Everything from generally suspicious behaviour to unauthorised access (including both people and vehicles) to irregular movement, violence, medical emergencies, vandalism to fixed assets, and even camera tampering can all be identified with the right solution.

AI-assisted video monitoring also allows teams to take a decidedly more proactive, responsive approach to security operators. Instead of waiting for a security event to be reported and reacting belatedly, AI-assisted operators can be notified when the event is still underway and respond as quickly as possible. By being in a position to protect an organisation’s assets before any damage is done, that organisation begins to save money immediately.

Finally, another one of the major benefits of AI-assisted surveillance is that it scales up incredibly well. You don't actually need to install AI-assisted monitoring software on all of your cameras. You can start with a smaller set of cameras first, then ramp up from there as your needs change. There is no initial need to commit to a massive change in your security infrastructure or to overhaul your entire system. You can take things slow, steadily building up as you feel comfortable. When the time does come to ramp up significantly, using AI-assisted video monitoring on a large number of cameras (like several hundred or more) is not a problem.

Conclusion

One of the most important things to understand with AI-assisted video monitoring is that even in this day and age, the most advanced computer systems are still not smarter than a human being. What looks like a major incident to the system may, through the context that the operator brings to the video feed, be a harmless event - the human intelligence allows this to be instantly determined. What AI-assisted video monitoring systems really excel at, however, is massively reducing the amount of extraneous information that operators must deal with, enabling them to make more informed and timely decisions.

It also generates an incredible amount of value for a business almost immediately. Not only will you save money by protecting your assets before any damage is done, but the productivity boost to your security teams alone makes taking the step towards implementation more than worth it.

 

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