Where are video surveillance cameras headed? At the core of next-generation Internet Protocol (IP) cameras are advanced chips with artificial intelligence (AI) at the edge, enabling cameras to gather valuable information about an incident: scanning shoppers at a department store, monitoring city streets, or checking on an elderly loved one at home.
Thanks to advanced chip technology, complex analytics operations are becoming more affordable across the full spectrum of surveillance cameras —professional to consumer — fueling the democratisation of AI in the IP camera market.
The video surveillance equipment market grew to $18.5 billion in 2018 and is expected to increase this year, according to IHS Markit. The latest research points to video everywhere, edge computing, and AI as the top technologies that will have a major impact in both commercial and consumer markets in 2019.
Computing at the edge means that the processors inside the camera are powerful enough to run AI processing locally, while still encoding and streaming video, and are able to do it all at the low-power required to fit into the limited thermal budget of an IP camera. New SoC chips will be able to perform all of the processing on camera and provide accurate AI information, with no need to send data to a server or the cloud for processing.
Instead, data can be analysed right in the camera itself, offering high performance, real-time video analytics, and lower latency — all critical aspects of video surveillance. This new AI paradigm is made possible by a new generation of SoCs, a key driver behind the market growth of IP cameras.
Microprocessor-enabled analytics allow users to more easily extract valuable data from video streams. How about an insider’s view into retail customer behavior? Consider video cameras at a department store, monitoring shoppers’ behavior, traffic patterns, and areas of interest. Next-generation cameras will recognise how long a shopper stays in front of a specific display, if the shopper leaves and returns, and if the shopper ultimately makes a purchase.
Next-generation video cameras will be able to create heat maps of stores to see where people spend the most time, so retailers will be able to adjust product placement accordingly. Analytics will also help identify busy/quiet times of the day, so retailers can staff accordingly. By understanding customers’ behavior, retailers can determine the best way to interact with them, target specific campaigns, and tailor ads for them. Cue the coupons while the shopper is still onsite!
City surveillance and smart cities are depending on advanced video surveillance and intelligence to keep an eye on people and vehicles, identify criminals, flag suspicious behavior, and identify potentially dangerous situations such as loitering, big crowds forming, or cars driving the wrong way.
Quick local decisions on the video cameras are also used to help analyse traffic situations, adjust traffic lights, identify license plates, automatically charge cars for parking, find a missing car across a city, or create live and accurate traffic maps.
When it comes to home monitoring, what will next-generation video surveillance cameras offer? Real-time monitoring and notification can detect if a person is in the back yard or approaching the door, if there’s a suspicious vehicle in the driveway, or if a package is being delivered (or stolen). Advanced video cameras can determine when notifications are and aren’t required, since users don’t want to be notified for false alerts such as rain, tree branches moving, bugs, etc.
Next-generation video camera capabilities can also help monitor a loved one, person or pet, helping put families at ease if they are at work or on vacation. For example, helpful analytics may be used to detect if someone has fallen, hasn’t moved for a while, or does not appear for breakfast according to their typical schedule.
When evaluating next-generation IP cameras (cameras on the edge), look at the brains. These cameras will likely be powered by next-generation SoCs chips. Here is what this means to you:
• Save on network bandwidth, cloud computing and storage costs. There is no need to constantly upload videos to a server for analysis. Analysis can be performed locally on the camera, with only relevant videos being uploaded.
• Faster reaction time. Decisions are made locally, with no network latency. This is critical if you need to sound an alarm on a specific event.
• Privacy. In the most extreme cases, no video needs to leave the camera. Only metadata needs to be sent to the cloud or server. For example, the faces of people can be recognised in the camera and acted upon, but the video never reaches the cloud. The cameras can just stream a description of the scene to the server “suspicious person with a red sweater walking in front of the train station, has been loitering for the last 10 minutes, suggest sending an agent to check it out.” This could become a requirement in some EU countries with GDPR rules.
• Easier search. Instead of having to look through hours of video content, the server can just store/analyse the metadata, and easily perform searches such as “find all people with a red sweater who stayed more than five minutes in front of the train station today.”
• Flexibility/personalisation. Each camera at the edge can be personalised to work better for the specific scene it is looking at, compared to a generic server. For example, “run a heat map algorithm on camera A (retail) as I want to know which sections of my store get the most traffic; and run a license plate recogniser on camera B (parking lot) as I want to be able to track the cars going in/out of my parking lot.”
• No cloud computing required. For cameras in remote locations or with limited network bandwidth, users have the ability to perform all analytics locally, without relying on uploading video to a server/cloud.
• Higher resolution/quality. When AI processing is performed locally, the full resolution of the sensor can be used (up to 4K or more), while typically the video streamed to a server will be lower resolution, 1080p or less. This means more pixels are available locally for the AI engine so that you will be able to detect a face from a higher distance than when the video is streamed off camera.
Professional-level IP cameras capable of performing AI at the edge are coming soon with early offerings making their debut at this year’s ISC West. As we enter 2020, we will begin to see the availability of consumer-level cameras enabling real-time video analytics at the edge for home use. With rapid technology advancement and increased customer demand, AI is on the verge of exploding. When it comes to image quality and video analytics, IP cameras now in development will create a next-generation impact at department stores, above city streets, and keeping an eye on our loved ones.
