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Solving the Nuisance Alarm Problem with PurifAI

Dec 10, 2024

Solving the Nuisance Alarm Problem with PurifAI: a Q&A with Chris Sincock, VP of Channel Development


Q: Why are nuisance alarms such a significant issue for the security industry?

A: Nuisance alarms present a significant challenge for the security industry, particularly for central monitoring stations. These nuisance alarms occur when systems mistakenly identify non-threatening events—such as moving shadows, animals, weather conditions, or lighting changes—as potential security breaches. This influx of nuisance alarms leads to alarm fatigue among operators, wasted resources, and delayed responses to actual threats. For central stations managing thousands of alarms daily, this situation is both costly with an average of 3 minutes for each nuisance alarm to be cleared, and dangerous as fatigued operators are more prone to miss actual alarms.


Q: What makes PureTech Systems' PurifAI a game-changer for addressing nuisance alarms?

A: PurifAI is an AI-driven video analytics SaaS specifically designed to combat nuisance alarms. Using patented, advanced machine learning algorithms, it reassesses alarms triggered by video motion detection and filters out nuisance events with remarkable accuracy. What’s revolutionary is its ability to nearly eliminate nuisance alarms without requiring any new hardware or on-site installation — it's a pure cloud-based solution.


Q: How does PurifAI integrate with existing security systems?

A: PurifAI is designed for seamless integration. It works with virtually any video surveillance system—cameras, DVRs, NVRs, and even other sensors. Since it operates in the cloud, you don’t need to invest in additional equipment or overhaul your current infrastructure. This makes it incredibly versatile and cost-effective for monitoring stations looking to improve their operations without major disruptions. If your alarm monitoring software can receive events via SMTP (email) it is a plug and play addition! For applications developers, PurifAI offers an API for sending videos and images to PurifAI and for receiving responses back indicating if the video and/or image contained an object of interest. This enables anyone building an application for any purpose to use PurifAI.


Q: What are the operational benefits of using AI-boosted video analytics and why should organizations look at it?

A: There are several. First, AI-boosted video analytics improves efficiency by reducing the burden of nuisance alarms, enabling monitoring stations to manage more sites without adding staff. This directly lowers operating costs. Second, when operators aren’t bombarded with meaningless alarms, they’re less fatigued and can focus on high-priority events, leading to better performance and job retention. Finally, AI-boosted video analytics’ ability to deliver reliable, verified alarms ensures swift response times to real security incidents.


Q: What sets PurifAI apart from other AI-driven video analytics solutions?

A: Many solutions claim to address nuisance alarms but fall short in versatility and effectiveness. PureTech Systems, the owner of PurifAI, is backed by over two decades of video for machine learning and algorithm development and stands out for its high accuracy rate, ease of deployment, and scalability. It’s a SaaS platform, which means you can scale as your needs grow without worrying about hardware limitations. Additionally, PurifAI works with alarms from any video motion detection source that can send alarms via SMTP (email), making it one of the most adaptable solutions on the market.


Q: How does PurifAI support scalability for growing organizations?

A: Since PurifAI is cloud-based, it’s inherently scalable. Whether you’re a small monitoring station managing a handful of sites or a large-scale operation overseeing thousands, PurifAI can adapt to your needs. The ability to add sites without the hassle of hardware upgrades makes it an ideal solution for organizations.


Read the full version of ISJ here:

 https://digital.internationalsecurityjournal.com/html5/reader/production/default.aspx?pubname=&pubid=cbb84b8f-eb1e-4eca-8fc2-1b863f3c1a00

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