Page 25 - Security Today, April 2022

TECHNOLOGICAL ADVANCES CHANGE THE GAME
Analytics are not new in concept, but their ability to be effectively implemented was severely limited by factors like bandwidth, processing power, camera quality, and storage issues. After all, even the most advanced algorithm in the world won’t produce much insight if it is being fed nothing but low-quality images.
Large amounts of data are needed to effectively train analytics, and storing that data can be expensive. Even organizations who could afford cloud storage solutions often ran afoul of bandwidth limitations as they uploaded massive amounts of video for processing. This combination of factors meant that, while video analytics had obvious potential, the technology needed to make them even more effective just wasn’t quite there yet.
Advances in edge processing power have changed this. Modern chipsets have improved to the point that much of the processing that only recently needed to be done in the cloud can now be done on the edge devices themselves, saving organizations both bandwidth and storage space. Instead of transmitting raw video to the cloud for analysis, cameras can now analyze the video directly and send only the relevant metadata to the cloud, making it easier—and more affordable—to store, categorize, and recall data when needed. These hybrid deployments balance the advantages of both the edge and the cloud—but more importantly, they have helped put modern analytics within reach
for organizations of all sizes.
It is also important to remember that cameras and sensors do
not function the same way that the human eye does. Instead, they classify things like color on number scales, and different devices may see things slightly differently, resulting in some data variance.
This is also changing as artificial intelligence (AI) becomes more advanced. Machine learning and deep learning models are becoming more common, and Deep Learning Processing Units (DPLUs) are revolutionizing what edge devices are capable of. They enable AI-based analytics to continuously improve their accuracy and performance by revising their datasets as new information becomes available. The dramatic improvement in the quality of both training data and learning models means that not only are analytics becoming more accessible to end users, but they are becoming increasingly capable of self-improvement, compounding their value over time with greater accuracy and new applications.
A SHIFT IN MENTALITY
While there are plenty of other factors to discuss when it comes to the emergence of analytics, it is important to at least touch on the role that the pandemic played. Over a very short period of time, businesses’ most pressing needs changed dramatically. Suddenly they needed to be able to count the number of people in the store, identify crowds and queues as
WWW.SECURITYTODAY.COM 25 Untitled-27 1 10/1/21 1:03 PM