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False alarms and AI

Updated: May 3, 2023


An alarm set off needlessly, it’s a false alarm – is it that simple too? The answer is a big NO.

False alarm rates cost a lot – one of the major drawbacks of security cameras is their waste of resources and inefficiency. More importantly, a large number of false alarms undermine the whole security efforts by distracting the focus. So, filtering the false alarms is not simple as it is.


Humans are good enough to decide whether someone is a real threat. In a blink of an eye – they analyze what they see… it’s like BINGO, got it!!!. But the story is not the same all the time. We depend on digital eyes to see things on our behalf and notify us and they do it better. But when analyzing whether something is worth monitoring, they fail to interpret the visual data within the relevant context. This raises false alarm rates.

A great solution for this is applying AI analytics, an innovative technology that follows human vision principles based on mass data analysis and is supported by machine learning.


AI is deployed in multiple ways. The AI algorithm is trained on larger, more accessible data sets. Then these complicated algorithms undergo an iterative process to derive patterns and features from the fed data. This helps the system to distinguish the objects and then filter a possible threat, and this reduces the false alarm rates.

  1. CLOUD AI: This is like a shared infrastructure – AI hardware and software are combined under a hybrid setup. This enables enterprises to access it easily and use all AI capabilities. This reduces the adoption cost and facilitates innovations.

  2. EDGE AI: This system depends on locally processed data. This model does not necessarily need internet options; instead, it relies on the data stored on them. Based on the situation, it analyzes in a short time and delivers the result. This eliminates the security issue of keeping a large amount of data in the cloud.

  3. ON-PREMISES VIDEO ANALYTICS: Cutting-edge video intelligence model, separates the video capture and AI analysis to multiple levels. Once data from the cams are served to the video analysis server, the data processing and filtering are performed to make a derivative.


False alarms cause a considerable drain on resources and cause substantial disruption, which affects businesses. AI can filter out motion noise to a great extent and critically lower the burden of financial and human resource waste on non-threatening events. Thus, the traditional way of alarm management is being overridden by the AI models, which can be a game-changer since the research in AI is going on a massive scale and is impacting all industries, the human race, and the alarm management industry can drain a lot from it.

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