Changing the Look of Security

Changing the Look of Security

Analytics can make searching video possible in real time, and what this means for you

Video analytics have been around almost as long as CCTV itself, rescuing security operators from the nearly impossible task of continuously and effectively monitoring video surveillance feeds. The concept of video analytics is simple enough: algorithms allow a device to analyze video in real time and send alerts to the user.

Analytics can also categorize aspects of recorded video so it is more easily searchable. While modern technology has made analytics more accurate and more affordable than ever before, we are still seeing some barriers to adoption. Here is how video analytics are changing the way we look at security, and how you can leverage this to provide more value to your customers.

History of Video Content Analysis

One of the earliest developed iterations of video analytics was motion detection. While video surveillance systems did correctly identify suspicious motion within a scene, such as that of an intruder, they were also great at identifying tree branches blowing in the wind, or rain falling in front of the camera lens. The prevalence of false alarms gave video analytics a bad rap, and rightfully so. Like the boy who cried wolf, video analytics were so unreliable that camera operators started ignoring them–defeating the purpose of having analytics at all.

Advances in hardware and software greatly improved accuracy, but for a time, the highest-performing analytics were only available in the most expensive equipment. Fast forward to today, where analytics have become mature enough that we are seeing them creep into the consumer surveillance market (see Nest, Ring).

On the camera and recorder side, better hardware lets devices react to event detection faster and more accurately. An increase in processing power allows for more sophisticated analytics at the edge. Inexpensive recorders work in conjunction and provide sophisticated suites of analytical capability. Now the only thing keeping end users from adopting video analytics is education.

Breaking the Barriers to Adoption

One of the most basic barriers that prevents users from incorporating analytics into their security system is lack of understanding. “Video content analysis” gets lost amongst buzzwords like “Artificial Intelligence (AI)” and “deep learning.” It is important to distinguish video analytics from the more advanced forms of AI. Technically, AI means “getting a machine to mimic human behavior in some way.”

Most video security equipment today uses rule-based analytics, and at the edge (embedded into cameras). This form of AI does not improve their analysis and accuracy through continued examination of data.

Rule-based analytics are a cost-effective solution that enables the system to examine many events and objects simultaneously in a way that would be impractical or impossible for humans to do. However, rule-based analytics do not have the cognitive ability to interpret activity as well as humans can.

The prevalence of video content analysis in procedural forensics crime dramas on TV has also negatively impacted the practical application of video analytics in real-life situations. The hopes an avid crime show watcher may have for their video surveillance system can be unrealistic. This “CSI effect” means that integrators should set expectations early on and focus on educating the user what the system can do instead of cannot do.

How Analytics Improve Return on Investment

Given the innovative abilities of today’s surveillance systems, users should be looking beyond simply getting a clear picture. They should be striving for a system that arms them with intelligence during the process of capturing video. This means not only installing devices with analytical abilities but learning how to use those functions as well.

A simple sales pitch is that video analytics improve the ability of a surveillance system to detect specific events in real time, and permit the user to easily search footage upon playback. Utilizing video analytics has an array of benefits: making the system operate more efficiently, reducing manpower requirements, providing business intelligence (such as improving customer service or better understanding staffing requirements), hastening forensic analysis and enhancing detection accuracy.

All of these attributes add up to increased ROI for the user, who can get more out of their security system and share the security budget across multiple areas of the business. This is an incredibly significant selling point to someone who is investing thousands or even millions of dollars into video surveillance.

Analytics at the Edge

Thanks to the hardware improvements and more accurate algorithms mentioned earlier, many highly-effective video analytics are available at even the lowest price points. Among the analytics that are most widely available are camera-based analytics, also called edge analytics. These are particularly well-suited to challenging locations or budgets because they require lower bandwidth and fewer servers. In addition, they are easily implemented in real time. The line crossing / tripwire function is one of the most commonly deployed edge analytics.

Expanding upon line crossing is perimeter protection, where analytics support detection of invasion in a defined area. Wellsuited for industrial parks, school campuses, factories and warehouses; perimeter protection employs algorithms that differentiate humans and vehicles. For example, enabling an object filter and selecting “human” will tell the system to send an alert when humans are detected and ignore vehicles and animals. Another filter could detect if vehicles are entering pedestrian areas and vice versa.

Today’s perimeter protection solutions have achieved better accuracy than previous versions, where frequent false alarms were caused by environmental conditions or wild/stray animals.

People counting is another easy-to-use edge analytic. The operational capabilities of people counting give users valuable data to run businesses, such as discerning how many people are gathering in a given area and even keeping track of how much time they have been standing there. By setting rules to trigger an alarm if, say, three or more people stand in line at the grocery store for more than three minutes, store managers can be instantly alerted when they need to send more staff to the cash registers and reduce wait times. Using people counting at the entrance to a nightclub or bar lets security staff know when the establishment is approaching the capacity defined by fire code.

Search and Review

Metadata – a set of data that describes and gives information about other data – collected through video analytics helps categorize and organize footage so that it is easier to search. When there is no known event to search for, surveillance systems produce a seemingly endless amount of camera footage and historical data that is not viewed at all. Now, video analytics allow for certain attributes to get tagged to the footage before it gets stored on the recorder. For example, face capture functionality can also store facial attribute metadata – such as gender, age, expression and whether the person is wearing glasses and/or has facial hair – along with a snapshot of the face. Once such metadata is introduced, smart search is possible.

Smart search opens a world of possibilities in locating very specific footage. Operators can find, say, vehicles detected in a tripwire area on a certain evening, or use the facial attributes function to display all clean-shaven people with glasses who passed through a particular train station in the past week. This will yield a thumbnail gallery you can examine in minutes rather than hours. Shortcuts like these let users efficiently review the data they have and glean deeper information from it.

Video analytics have given rise to an exciting advancement in playback: a time-compressed view where moving objects are tagged with the time of day, such as BriefCam’s VIDEO SYNOPSIS and RapidRecap from Lorex. A report of a thirty-minute sequence, for example, generates a video synopsis that is only 53 seconds long because the view simultaneously displays events that have occurred at different times. People and vehicles that move in and out of the field of view are superimposed on a stationary background along with a time stamp “affixed” to them. It’s a fascinating glimpse into the future conveniences video analytics may have yet to offer.

It is Up to Us

Video analytics are truly changing the way we use security. They turn our security cameras into data collectors that help businesses run more smoothly. They save time by only giving us the information we need, whether we need to know about it as soon as it occurs, or after the moment has passed.

Now that analytics are less likely to produce false alarms and are more widely available in budget-friendly products, a growing number of end users can benefit from them. It is up to us, the manufacturers and installers, to clear up misconceptions and show the true value that analytics can provide.

This article originally appeared in the October 2019 issue of Security Today.


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