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Security Video Analytics using artificial intelligence are a hot topic in the industry but do you know how they work and their value, especially as they relate to reducing risk and saving money. In reality, moving the dial on risk and cost is incredibly challenging. While it’s relatively easy to create AI and machine learning models, it’s significantly more difficult to build the annotation data that drive the AI. What’s more, AI generally performs well in perfect conditions, but what happens when angles are bad, lighting is poor, resolution is low, or camera quality is questionable? That’s a huge consideration, since most models depend on perfect conditions to work. Adding to the complexity is the use of personally identifiable information (PII) to build the annotation data, which is rife with legal and ethical questions. In a perfect world, you’d source you own video, annotate yourself, and ensure perfect conditions exist. But how realistic is that? And then what is your exposure to risk? How many nuisance alerts or issues will you tolerate?
In this webcast, Mr. Kevin Stadler, President & CEO, Evolon Technology, Inc., will peel back the layers of these questions and take a hard look at the reality of AI modeling. We’ll look into how most models work, questions around annotation, and the application of advanced algorithms and features such as adjustable thresholds that can affect the risk/cost sliding scale. You’ll come away with a better understanding of this complex topic and be better prepared to inform your clients.
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