Artificial Intelligence (AI) continues to gain momentum as more organizations and consumers experience the significant benefits of intelligent assistants and the operational efficiencies and cost savings brought about by such technology. For the security industry, and for video surveillance in particular, AI-based technology is making a substantial impact. At its core, an AI algorithm’s ability to “learn” from data it is presented with represents a sea change event in the evolution of security systems. The term “machine learning” derives from that most basic idea. Deep Learning, a subset of machine learning based on neural networks, is frequently used to analyze and compare image data. Utilizing deep learning algorithms enables cameras to ‘see’ in radical new ways. From reducing noise and blur in images by identifying object motion, to the detection of people and vehicles and eliminating false alarms, these technology tools give operators the ability to work smarter and more efficiently than ever before. But it doesn’t end there. Beyond security, AI cameras can also function as smart sensors that deliver intelligent insights regarding operations, sales and marketing for organizations.

 

It’s Time to Think Differently About Security Cameras
Traditional digital cameras can’t identify the objects they capture. They just blindly record pixels to disk. With analytics, if the camera sensor detects movement in those pixels, it can place a bookmark in the recording or send an alert. Traditional motion-based analytics can be prone to false positives. Depending on where a camera is installed, a motion event can be triggered by something as mundane as a shadow from a passing cloud. For this reason, many security professionals have had to carefully consider where they deploy motion-based analytics in the past. AI-based object detection has fundamentally changed the rules for analytics, allowing cameras to be installed anywhere with little risk of generated false alarms.

 

AI-based technology can fundamentally change the value proposition of a security system. With such powerful technology on the edge, cameras and analytics not only perform better, but they can also capture new types of data that change how loss prevention specialists and security teams operate. AI-based analytics never blink and serve as an extra set of eyes for security personnel. As more cameras are installed, having an intelligent assistant that never sleeps becomes an essential tool of the trade.

 

AI-based cameras from Hanwha Techwin can reliably identify objects such as a car, truck, bicycle, license plate or a person. P series camera models can also discern additional unique attributes of those objects, like color or whether a license plate or face is present. With their myriad new potential to protect and inform, it’s time to think differently with regards to the value these devices can bring to an organization. 

Real-time Event Notification
When it comes to protecting assets and people, real-time alerts can travel through a video management system (VMS) like Wisenet WAVE and ping a mobile device. This enables security teams to be more proactive rather than reactive as events unfold in real time. Because AI-powered analytics all but eliminate false alarms, they can more accurately determine incidents that require further investigation. With the additional data AI-based cameras can capture, sophisticated rules can be written for analytics to deliver precisely what is required for a given scenario. For example, we can tell a camera to ignore people, but to issue an alert when a truck enters the loading dock. AI can also help us count objects like people or cars more precisely than ever before. This includes the ability to count objects accurately even when they partly “occlude” or pass in front of each other. This is far superior to conventional people counting techniques, which required a top-down view to avoid occlusion and rendered the cameras useless for identifying faces. It’s also key to accurately counting vehicles when managing congestion.

 

Post-Event Search
When it comes to post-event forensic searches, AI-based cameras provide unique advantages. Additional descriptive metadata about objects is captured within each frame. That metadata, which might include descriptive characteristics of objects like the color of a person’s shirt or pants, their gender or approximate age, enables an operator to quickly search through video to find a particular object or person in seconds. It’s important to utilize a VMS that can interpret and search this descriptive metadata, such as Hanwha Techwin’s Wisenet WAVE or Genetec’s Omnicast.

 

Beyond Security
Although people counting, heat maps and queue management analytics have existed for some time, they too have been subject to the inherent inaccuracies of pixel-based motion detection. Conversely, AI-based object detection delivers highly accurate data and metrics for operations, sales and marketing teams looking for insight on everything from retail store performance to ensuring process efficiency and operational compliance. As a result, these cameras have become an indispensable tool for business operations. Depending on the organization, the business value of such data can be worth many times the cost of the system. 

 

For customers with more sophisticated data analysis needs, camera metadata can be accessed and combined with other data and processed by other platforms for sophisticated visualization and data mining. This allows technology partners to access the aggregated data into their own charts, graphs and exception reports powered by specialized software they may already be using. There are familiar use cases spanning multiple industries that require linking data from access control, intrusion, point of sale systems, staffing data, schedule data, weather data and many other data sources. The potential for this unified data to create comprehensive business solutions is substantial. 

 

Benefits of Edge Processing
With AI on the edge, valuable events and other metadata generated at the camera can be gathered from many endpoints and aggregated together to enable clear visualizations of the trends and anomalies. This can be done on a lightweight local server that also runs the VMS. Running AI analytics at the camera significantly reduces the overall cost of the equivalent server resources required to run AI-based analytics since edge-based analytics run before video is compressed and streamed. Many Hanwha Techwin cameras include license-free analytics as standard.

 

Running the same analytics on a server requires that the video streams be first decoded which requires CPU/GPU resources that can scale dramatically as stream count increases. While the power of a server far outweighs what a camera can provide, there is a point of diminishing returns when electing to do everything on a server for all but the most demanding processing. For that reason, a hybrid approach in which AI analytics are performed on the edge and the lightweight data results are sent to an inexpensive server or workstation for aggregation and display will remain a popular choice for some time.

 

Re-imagining Opportunities
With these new ways to collect actionable data, issue real-time alerts and save countless hours combing through footage, it’s time to re-imagine video security system deployment opportunities. AI-based cameras have become important data collection sensors which protect and inform organizations in new ways. Their ability to identify and count objects, display heat maps and ensure process and operational compliance brings a new dimension to what was once seen as a cost of doing business. Beyond state-of-the-art security and loss prevention, AI cameras are now taking their place as an enabling technology for revenue generation while enhancing business operations and logistics.