Video analytics enables computers to see and understand the world like humans. The software analyzes image data pixel by pixel to identify patterns and objects in real time.
Security systems using intelligent video analysis can prevent false alarms by eliminating 90% of them. They also enable organizations to save money by reducing staffing costs and improving productivity.
Preventing Crime
Video analytics software has the potential to prevent crime by identifying situations that don’t conform with what is expected of the environment. For example, if an individual enters a store carrying an AK-47 and no one is there to stop them, the software will alert first responders immediately.
Unlike humans, machine learning algorithms aren’t distracted or tired, meaning they can keep working at it all day without getting frustrated. Using intelligent software to filter out false alarms also frees up time for operators to work on other tasks, or to focus on responding quickly to real events.
Using smart image/video analytics reduces the amount of visual data that must be reviewed by human operators, reducing response times significantly. And with the ability to run locally on-device (Edge Computing), this technology can also be deployed at sites where internet bandwidth is limited. This allows it to operate more seamlessly and be used in a wider range of applications.
Detecting Crime
Video analytics technology can also help prevent crime – in retail it can stop shoplifting; in hospitals it can guard against theft of drugs or even patient abductions and a number of organisations have been using it to spot elderly people who might fall and need a call out.
The key is that this type of software can see things that the human eye simply cannot. It does this by isolating a series of freeze frames and then analysing them with an engineered decision tree. This asks a series of questions to determine whether there is a threat on site and then alerts the operator.
The result is that the Video Analytics Definition human operator doesn’t have to deal with the countless false alarms that occur with conventional CCTV and they can focus on the genuine incidents. This not only reduces risk but saves staffing costs and business disruption. It also reduces the need for costly equipment and the reliance on outside security providers.
Predicting Injuries
Video analytics helps organizations reduce workplace injuries by ensuring that individuals comply with health and safety policies. For example, video analytics can detect whether workers have met the required hand washing protocols or if they are wearing masks and eye protection in chemical-related areas. It can also monitor workers for signs of fatigue and notify them to take a break or stop working.
Many video analytics solutions can be deployed to monitor real-time events or to search for incidents that occurred in the past. This can be done centrally by a server or, more often, on the cameras themselves through edge processing – an approach that can speed up decision making. Regardless of the deployment strategy, a solid foundation for data integration is needed to make this work. This is where platform as a service (PaaS) comes in. It can help simplify the complexity of tools and accelerate the deployment of machine learning for video analytics.
Preventing Fraud
Video analytics is a powerful tool that helps prevent fraud. It can identify e-commerce fraud, such as stolen credit card numbers and other security breaches, by scrutinizing online transactions. This allows businesses to take proactive measures and ensure the safety of customers, employees, and assets.
Traditional systems require human operators to manually review video footage to get business intelligence. This is a time-consuming process and often forces decision-makers to act on data that is days or even weeks old. Video analytics technology automates this task, allowing decision-makers to react in real-time.
Intelligent video analytics also cuts out the 90% of false alarms that humans have to deal with. This helps reduce the stress of operators and makes them more alert and capable when dealing with actual incidents. Unlike humans, machine learning algorithms don’t get tired or lose focus and are able to deliver consistent detection support for as long as the need is there.
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