Page 52 - Security Today, October 2019

Changing the Look of Security
Analytics can make searching video possible in real time, and what this means for you
BVy Jennifer Hackenburg
ideo analytics have been around almost as long as CCTV itself, rescuing security opera- tors from the nearly impossible task of contin- uously and effectively monitoring video sur- veillance 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 ana- lytics 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 lever- age 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 in- truder, they were also great at identifying tree branches blowing in the wind, or rain falling in front of the camera lens. The preva- lence 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 pur- pose 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 an- alytics have become mature enough that we are seeing them creep
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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 incorpo- rating analytics into their security system is lack of understand- ing. “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 analyt- ics, 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 foren- sics crime dramas on TV has also negatively impacted the practi- cal application of video analytics in real-life situations. The hopes
VIDEO ANALYTICS
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