Beyond the Basics

Beyond the Basics

Understanding the analytics behind the networked security camera

A security camera is much more than a security camera when it’s part of an analytics infrastructure that serves completely new goals, advancing the organization and management of businesses and even entire cities. Many end users are surprised at how much data can be mined from an innocuous camera on the ceiling or on the street corner.

Just as computers and VMS continue to get “smarter,” so too are the cameras themselves, evolving their capabilities to see beyond the pixels they capture. Analytics on the edge is nothing new, and most respectable security cameras have a variety of ways to detect motion and basic behaviors of objects in the frame. They can detect loitering, direction of travel and can specify zones to pay attention to.

The latest cameras also detect sound events and can warn users of gunshots, screams, explosions, and glass breaks. These are all very valuable from a security standpoint and will continue to evolve in the coming years. What many don’t realize is that the ability to completely customize a camera to become a business or city management analysis tool is also making great strides.

Giving Cameras a New Job

Basic edge analytics for security are typically built-in to today’s security cameras. Going beyond security, there are a number of specialty analytics such as license plate recognition (LPR), traffic, people counting, and heat maps that can be made available to provide new levels of business intelligence and city management. Buying a camera that is purpose-built for business analytics or LPR is possible, but not always practical.

What if you want your LPR camera to also count people? The answer is to let end users choose the analytics they require from a collection of best-of-breed third-party developers. This has the benefit of reducing the material cost of manufacturing specialty cameras and also simplifies the installation and future upgrade potential. Basically, we want to design a camera for all seasons where users can install custom analytics applications as simply as one installs an app on a smartphone.

These applications have access to the raw video coming from the sensor and can analyze it onboard of the camera. At Hanwha, we call this capability the Hanwha Open Platform and it is a core feature of our Wisenet X and P series cameras. Hanwha engineers have purposefully reserved space in our Wisenet 5 chipset to allow open platform applications from third parties to be installed as required by the end user. Not only does it unlock powerful new tools and features, it also enables customers to pay for only the analytics they need, when they need them.

This capability, while not exactly new, is seeing rapid adoption as more integrators and end users discover the business value inherent in what was previously a security expense. A security system that delivers actionable intelligence about businesses and city infrastructure can rapidly pay for itself and even become a revenue generating tool in some instances.


So, what types of analytics can third-party vendors provide today? Most people are familiar with license plate recognition cameras and servers. Depending on the requirement, an application like Arteco LPR can integrate seamlessly into a Wisenet X series camera providing license plate recognition at speeds up to 80 miles per hour. It can work very well for traffic management, parking management, access control, vehicle tracking, and crime prevention.

For a different type of traffic monitoring, the Automatic Incident Detection (AID) application can detect stopped vehicles, slowdowns and congestion, wrong way drivers, pedestrians, smoke/low visibility, and lost cargo. The application collects data on traffic flow such as vehicle counting and classification, traffic density, average speed, and more.

In the retail space, business analytic applications like those from RetailFlux enable people counting, heat maps, shopper flow, route maps, zone analysis and queue monitoring. No records are kept about shopper identities, only actionable insights about what is working and what needs improvement.

Most of these analytics applications also come with their own front-end software or server to collect and display the data in a way that is useful for end users and stakeholders. Integration with popular video management systems is common as a way to dashboard the information display for ease of use.

The Future Looks Bright for Analytics and Metadata

All of these applications share one thing in common: They collect metadata. Metadata, at its core, is simply data about data. As technology continues to evolve, metadata collection and analysis will become a more important and necessary part of the successful management of cities, businesses, and security.

Combing through surveillance video for an event that occurred sometime in the last 48 hours is a laborious task for anyone. Today, metadata generated by the camera, whether it be motion or sound trigger marks, helps staff find events quickly.

In the future, as we see new developments in AI and Deep Learning, we can expect these on-edge analytics to take a big leap forward in the types of metadata they are able to extract. Instead of simply marking up motion events, future applications will utilize object classification techniques to detect physical objects like a chair, car, bus, motorcycle, train, human, cat or dog. If you have a little bit of information about the scene—red car, person wearing black pants and green jacket, dog barking—this will enable VMS to quickly find footage of interest, all thanks to the rich metadata being captured by the cameras.

Another aspect of deep learning is about how we store and restore compressed video data. Storing RAW footage from the camera, particularly as greater than HD formats become common, is not practical due to immense file sizes. We already compress/encode into H.264 and H.265 today because of need for saving bandwidth and storage space. By the very nature of compression, we are discarding some data in the image that is typically not important to how the eye sees. With deep learning, we can imagine how an AI algorithm can learn how we have encoded and compressed an image and then be able to restore even compressed areas of an image during live viewing and critical analysis.

The best thing about technology is that it never stands still. The applications-based model for installing purpose-built on-edge analytics, on demand, is here to stay and should be an important consideration as you plan for future deployments and upgrades to existing security infrastructure.

This article originally appeared in the May 2018 issue of Security Today.


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