AI-based solutions are becoming more common, but solutions in the security industry have been leveraging AI for years, and they just use the word “analytics”. As businesses seek new ways to create competitive advantage using AI, many are beginning to recognize that video devices represent increasingly valuable data sources that can generate actionable business intelligence insights. With improved processing power and chipsets becoming higher, modern IP cameras and other security devices can support AI-powered analytics that far exceed the identification of intruders and shoplifters.
Many businesses have leveraged AI-based analytics to increase efficiency and productivity, reduce responsibilities and better understand their customers. Video analytics can help businesses identify ways to improve employee productivity and staffing efficiency, simplify the layout of stores, factories and warehouses, identify required products and services, detect faulty or poorly maintained equipment, and then break them, and more. These new analytics features are designed with business intelligence and operational efficiency in mind, and organizations of all sizes are increasingly accessing them.
AI’s accessibility in video surveillance
Analytics always have clear applications in the security industry, from basic intelligence and video motion detection to more advanced object analysis and deep learning, allowing modern analytics to identify suspicious or criminal behavior or discover suspicious sounds such as destroying glass, gunshots, gunshots or cries for help. Today’s analytics can detect these events in real time, alert security teams immediately, and significantly reduce response time. The advent of AI has enabled security teams to be more proactive, allowing them to make quick decisions based on accurate real-time information. Not long ago, only the most advanced surveillance equipment was enough to run the AI-based analytics needed to implement these functions, but today, the landscape has changed.
The emergence of deep learning processing units (DLPUs) significantly enhances the processing power of monitoring devices, allowing them to run advanced analytics at the edge of the network. Just a few years ago, the bandwidth and storage required to record, upload and analyze thousands of hours of video could be extremely expensive. This is no longer the case: Modern devices no longer need to send full video records to the cloud, it’s just the metadata needed to classify and analyze. As a result, the need to utilize the bandwidth of AI-based analytics capabilities has greatly reduced storage and hardware footprints, whether using three cameras or three thousand networks, greatly reducing operating costs and allowing enterprises of all sizes to use the technology.
As a result, the scope of potential customers has been greatly expanded – these customers are not only looking for security applications, but also business applications. Since DLPUs are actually standard on modern surveillance devices, customers are increasingly looking to leverage these features to gain a competitive advantage in addition to protecting their location. The democratization of AI in the security industry has led to a significant expansion of use cases as developers want to meet the shift to video analytics in enterprises to address the broader security and non-security challenges.
How organizations use AI to enhance their operations
It is important to emphasize that part of the reason that makes more business-centric use cases of AI-based video analytics emerge is that most businesses are already familiar with the basic technologies. For example, retailers who have used video analytics to protect their stores from shoplifters are pleased to learn that they can use similar features to monitor customers entering and leaving the store, identify high and low traffic periods of traffic, and use that data to adjust their staffing needs accordingly. They can use video analytics to remind employees when long queues form, when empty shelves need to be resuspended, or when the store layout causes unnecessary congestion. By connecting with security-centric analytics, retailers can improve staffing efficiency, create more effective store layouts and enhance customer experience.
Of course, retailers are just the tip of the iceberg. Businesses in almost every industry can benefit from modern video analytics use cases. For example, manufacturers can monitor factory floors to identify inefficiencies and choke points. They can use thermal cameras to detect overheated machinery, allowing maintenance personnel to resolve issues before causing significant damage. In many cases, they can even monitor products with defective or poorly manufactured assembly lines, providing an additional layer of quality assurance protection. Some devices can even monitor chemical leaks, overheating devices, smoke and other signs of danger, thus keeping tissue from potentially dangerous (and expensive) events. This has clear applications in industries ranging from manufacturing and healthcare to housing and critical infrastructure.
The ability to generate insight and improve operations goes beyond traditional business and healthcare. Now, hospitals and healthcare providers are using analytics to perform virtual patient monitoring, allowing them to look at patients within 24 hours. Through the combination of video and audio analysis, they can automatically detect painful signs of coughing, dyspnea and pain. If a high-risk patient tries to leave the bed or leave the room, an alarm can be generated, allowing the caregiver or safety team to respond immediately. This not only improves the patient’s prognosis, but also significantly reduces the responsibility for slip/trip/fall cases. Similar technologies can also be used to improve compliance outcomes, ensure emergency exits are kept clear and avoid other potential health care and other industry crimes. Opportunities to reduce costs and improve results expand every day.
Maximize AI in the present and future
With most organizations already familiar with the equipment needed to leverage their advantages, a rapid shift in surveillance devices for business intelligence and operational purposes has taken place toward business intelligence and operational purposes. And, with businesses of all sizes (in almost every industry), they will find video analytics to enhance their security capabilities and business operations, so new, AI-based analytics development is unlikely to slow down anytime soon.
Most importantly, the market is still growing. Even today, about 80% of the security budget goes to manual labor, including monitoring, protection and maintenance capabilities. As AI-based video analytics become more common, such analytics will change very quickly and businesses will be able to simplify their business intelligence and operational capabilities in a similar way. As AI development continues and new, business-centric use cases emerge, organizations should ensure their location to make the most of analytics (now and future).
