Ease of Inspiration

From the practical solutions today to tomorrow’s app store

Inspiration can come from many avenues. A personal experience. Frustration with the status quo. A friend. A chance encounter. Sometimes even from our dreams. The trick is turning that inspiration into action.

The world of video analytics has suffered from an inspiration-to-action dilemma since September of 2001. To be fair, it was the unrealistically high expectations that were set for analytics that made action nearly impossible. There were dreams and promises of Minority Report-like video intelligence that could predict a crime before it happened. Fortunately, industry expectations have come back down to earth, and we’ve learned some valuable lessons along the way.

to say the video analytics industry has been a failure this past decade would not only be wrong, it would be insulting. Security, operations and loss prevention professionals are using a cavalcade of analytics to great success, ranging from basic to advanced to what would seem futuristic to most.

Basic Analytics

Video motion detection. Nearly every IP-based digital surveillance system today uses some form of video motion detection. Whether VMD is enabled to protect specific critical assets or better control bandwidth and storage, motion detection proves its worth. Traditionally, video motion detection has relied on pixel-based change in an image, but the latest algorithms actually act as true motion detectors, improving accuracy.

Tampering alarms. Likely the most underused and underrated analytic of the bunch, a tampering alarm alerts security guards if the camera has been manipulated in some way. The camera learns the scene and can tell if the pixels have been changed— as when someone covers or spray paints the lens, or even turns the camera to a different position. The camera can also alert the system if it has been knocked out of focus or if it’s not sending a video signal at all—but unfortunately not many users have set up this simple configuration.

Audio detection. While laws and regulations across many regions of the United States prohibit the recording of audio, the use of sound detection is widespread. This analytic detects if a sound reaches a certain decibel level and alerts the system owner. In its elementary form, it’s perfect for catching late-night prowlers. And while audio detection is classified as basic here, developers have added features to move this analytic more into the advanced category with what could be classified as Audio Detection 2.0.

Some municipalities are using audio coupled with PTZ cameras to detect gunfire and initiate the camera to automatically shift its field of view to where it heard the sound. Even more advanced analytics are hitting the market to detect threat levels in a voice by analyzing the audio pattern to determine a person’s stress levels.

Advanced Analytics

Cross-line detection. Drawing virtual trip wires drawn in the camera’s field-of-view seems like a simple task, but the trick is to configure and identify an object’s direction. In low-traffic environments, this application runs inside the camera or encoder to detect when an object crosses a virtual line while moving in a specific direction set by the user. It’s perfect for building entrances, loading docks, parking lots, roads that abut school zones and restricted areas.

Heat mapping. Unrealistic expectations were a big reason for analytics’ false start, but the need for near-perfect accuracy was another. If a bag left behind is missed, there could be disastrous consequences. In the world of store operations and marketing, however, there’s more room for error—and more room for growth.

Retailers, while lagging behind other verticals when it comes to IP video adoption, are enjoying novel uses of analytics. Specifically, heat mapping can track customer movement around the store and create an overlay to show hot and cold zones with traffic patterns. This data is invaluable for merchandisers and can give the marketing department reason to take interest in the surveillance system.

People/object counting. People counting is another analytic prevalent in the retail world, and applications such as Aimetis’ People Counter are becoming increasingly accurate as more advanced algorithms are installed on more powerful cameras. For example, retailers can use it to determine staffing levels during peak hours. Universities are using the same technology to count cars entering the parking garages on game days to better manage capacity, and, like retailers, some K-12 schools and nonprofits use people counting to prove that more funding is needed to staff afterhours or special events.

Future-is-Now Analytics

License plate recognition. LPR-specific cameras have been around for years, but they are expensive and analog-based. Today, LPR analytics—like the one recently released by ipConfigure—are designed to run inside a high-resolution network camera, making the camera usable for multiple surveillance needs while opening the doors to additional applications. Typical LPRs will still be used in toll collections, parking garages, campus environments and city surveillance applications, but emerging applications can be found in gated communities, after-hours check points, longhaul container tracking, visitor management, gas station reward programs, and even automated will-call at restaurant curbside carry-outs.

Fire and smoke detection. Yes, smoke and fire detectors have done their jobs for years. But in large, indoor expanses, it can be difficult to pinpoint the source of the fire as smoke drifts to several surrounding detectors. Analytics that leverage video verification of smoke and fire, such as Fike’s SigniFire, can alert operations to the source so it can be quickly extinguished while the building is evacuated.

Video synopsis. This is an application you need to see to believe (and to fully understand). The leader in the video synopsis space is BriefCam, whose software detects events throughout a predetermined period of time—say, 24 hours—and then overlays all the events on top of one another to enable security professionals to browse hours of video incidents in a matter of minutes. To help police operations and surveillance in airports and cities keep track of all that’s going on in the scene and pinpoint a specific event, time signatures appear over the object in motion to indicate the time of day.

Behavioral analytics. Behavioral analytics like the ones installed by BRS Labs in San Francisco and Louisiana’s Greater Lafourche Port Commission might seem like a Minority Report-style prediction analytic, but, just like a person observing a scene, the analytic learns the normal and routine activity from the day-to-day video data it captures to identify when something doesn’t look right. It still requires the use of an operator to determine if what the camera detects as out-of-the-ordinary is in fact suspicious, but it calls the operator’s attention to actionable video and keeps eyes fresh and minds sharp.

The Cutting-Edge

In only a few years, the saying “There’s an app for that” has become cliché. If you haven’t said the phrase, you’ve likely rolled your eyes at someone who has. But the saying exists for good reason. We have seen much inspiration from developers of software applications using the Apple iOS and the Android platforms. When we face a challenge in our everyday lives, we run to the Apple App Store or Google Play to find a solution.

Why can’t there exist that ease of inspiration in the surveillance world? Unfortunately, the list of real-world surveillance analytics pales in comparison to the 40 billion-plus apps downloaded onto smartphones and tablets.

Yet the concept of the network camera or encoder mimicking an app store model is not new. Embedded motion detection has been running inside the camera/encoder (i.e., “at the edge”) for almost 10 years. More applications have become embedded, including tampering alarms, cross-line detection, audio detection and local storage capabilities.

Not until the emergence of the latest application-specific integrated chips (ASIC)—that is, a processing chip specifically designed for the surveillance world—has the potential been this great. Following the path of Moore’s Law, the processing power of these ASICs has been doubling every 18 months, and we now have enough processing performance to open up a new world of application opportunities.

What are the advantages of running an application at the edge? First, traditional analytics can require many CPU cycles, which limits the number of cameras supported per server. Offloading some of those CPU cycles onto the camera or encoder itself and processing all or some of the video at the edge reduces the cost of the total solution.

Second, the solution will be far more scalable as less space is required in the server room. Cameras and encoders can be updated to meet the latest firmware and features and moved to different hot spots, depending on specific data needs.

Third, the surveillance customer may not have the option of having a local server where the camera is installed, but devices can be remotely accessed when needed without any additional hardware required.

As IP cameras continue to become increasingly powerful, the guiding vision is to create a surveillance app store that inspires developers to look at this market as a target for their talents.

Video analytics was once the great promise of digital video to make our systems more intelligent and our personnel more efficient. Until that is achieved, we need to think outside the traditional surveillance world, share our unique challenges with each other and turn inspiration into action. If not, when the time comes that you see a revolutionary yet surprisingly simple analytic solution, you’ll be asking, “Why didn’t I think of that?”

This article originally appeared in the October 2012 issue of Security Today.


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