Page 38 - Security Today, November/December 2021

Yesterday’s Hype How analytics has become today’s reality
By Rob Muehlbauer
Let’s admit it. When analytics first hit the market, they got off to a rocky start. Lots of hype, and high expectations. When it came to delivering on those promises, their performance often fell short of the mark. Despite this somewhat checkered past, enthusiasm for analytics’ potential has not waned. Over the last five years, it has begun to pay off. The analytics of today are very different from their early predecessors. So what has changed?
A MUCH-IMPROVED VIDEO ECOSYSTEM
It all comes down to better hardware and more advanced software tools. The trajectory of improvements in the entire technology ecosystem – lenses, sensors, chipsets and development platforms, as well as machine and deep learning modules – are making it possible for today’s analytics to be far more accurate and reliable than their predecessors.
Lens technology. It starts with matching the right lens to the surveillance task to ensure the capture of quality images. Advances in optics have led to more choices: lenses with varying fields of view, different levels of zoom, even levels of sensitivity to light. They are also yielding better resolution, contrast, clarity, and depth of field.
Sensor technology. Sensor technology has also improved. Today’s sensors deliver greater dynamic range, capturing usable image details even in bright light and shadows. They also outperform their predecessors in eliminating digital noise and capturing color in lowlight conditions.
Chipsets. Today’s chipsets are specifically optimized for surveillance applications. They provide far more horsepower than previous iterations. They can run complex algorithms quicker, with a higher rate of accuracy. They can take the raw data from the sensor, improve the image with more saturated and realistic color, provide clearer images of moving objects, and enhance detail in backlit scenes and scenes with big differences between light and darkest areas. In addition, they support enhanced compression technologies – H.264 and H.265 – allows for transmitting higher resolution images at lower bandwidths.
Open development platform and artificial intelligence. On the software side, today’s development platform provides more flexibility for modeling algorithms, testing and revising them to improve their accuracy. Drawing on artificial intelligence – both machine and deep learning – developers have been able to create algorithms capable of figuring out relations between data, recognizing images and patterns, and deducing high-level meaning.
What’s more, it is important to note that open development platforms – by their very nature – foster innovation. This is because open-source communities encourage contribution and collaboration, thereby benefiting from many contributors. As a result, open-source projects capitalize on diverse-thinking, which can accelerate the development process versus projects conducted
in isolation or within the confines of a proprietary system.
INTEGRATION WITH
OTHER SECURITY TECHNOLOGIES
Because they are yielding dependable results, analytics have begun playing a larger role in the marriage between surveillance cameras and other security technologies.
Access control. Analytics are being incorporate into multi- factor authentication systems. Facial recognition analytics can rapidly compare a visual database of card credentials against live video to determine the authenticity of the user.
Network audio. Analytics are becoming part of a proactive verbal deterrent system. They are able to recognize movement, discern between a person and an animal, and trigger a network audio recording to warn away an intruder.
Radar. Analytics serve as the link between radar and the video camera. When radar detects motion, analytics can trigger a network camera to auto-track movement based on coordinates from the radar.
Environmental sensors. Integrating analytics with environmental sensors will determine the threat level of changing conditions – everything from snow and tornedos to flood conditions, anomalies in traffic patterns, even air quality.
Audio analytics. Now, in addition to video analytics, emerging audio analytics are taking sound detection to new levels. Instead of simply measuring decibel levels, they are able to distinguish and alert on specific wave patterns such a weapon firing, glass breaking, a car alarm or aggressive voices.
SETTING REALISTIC EXPECTATIONS
Even though analytics have come a long way, given the industry’s history, it is important to set realistic expectations about what specific analytics can and cannot do. Integrators and installers need to do their due diligence.
Review the reference materials from manufacturers and developers – manuals, installation guides, etc. – to help you understand best use cases. Talk to their technical staff for
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ANALYTICS
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