Unlocking Central Monitoring's Potential

Central Monitoring Stations have entered a new era of efficiency, driven by the power of Artificial Intelligence/Machine Learning (AI/ML)- boosted video analytics. ML involves using advanced algorithms designed to improve performance and accuracy through experience and training data.

Video analytics systems start with an initial dataset comprising of examples with defined objects, actions, or events to recognize. These labeled examples encompass a wide range of scenarios, from people walking and animals to intruders entering secured areas. This initial training phase serves as the foundation upon which a system's profound understanding of video content takes shape.

Once the system is equipped with this foundational knowledge, it utilizes various techniques for feature extraction from video frames, such as object shapes and motion patterns. These features serve as the building blocks for making predictions and identifying objects or actions within the video feed. The system also selects appropriate ML algorithms, such as convolutional neural networks (CNNs) tailored to specific use cases like object detection, tracking, or activity recognition.

But here's where the magic truly unfolds: a feedback loop, comparing these predictions with the desired to measure the accuracy of its predictions. In times where discrepancies arise, it learns, adapts, and evolves. This might look like adjusting algorithm parameters, refining feature extraction methods, or incorporating new data. This constant journey of adaptation and perpetual learning leads to improved accuracy and reliability.

How Do Video Analytics Really Work?

Now, picture a bustling Central Monitoring Station, where video analytics systems take in and process a steady stream of un-labelled video data from diverse sources. Drawing from learned knowledge, they effortlessly provide predictions on the presence of people, vehicles, and specific behaviors within the video feed.

By harnessing the power of advanced computer vision algorithms and ML, video analytics distinguish between true security threats and nuisance/false alarms with precision. Once an object of interest is autonomously detected, the auto-verification process of said object begins. Using ML as explained above, the object will be classified and tracked, while simultaneously issuing an alarm for operators to take swift action.

With PureTech Systems’ PurifAI, PureTech’s patented and award-winning AI/ML-boosted video analytics seamlessly integrates with your existing infrastructure, eliminating the need for an extensive overhaul. It's as simple as adding PurifAI to the loop to instantly transform your Central Monitoring Station.