The Advantages of Virtual Barrier Video Analytics
Learn how new video analytics technology overcomes the challenges of traditional motion-based analytics
Artificial intelligence and machine learning have transformed the perimeter security landscape through the use of video analytics for intrusion detection. Highly-sensitive motion-based analytics, capable of detecting microscale movement at impressive ranges, have seen widespread adoption. However, that technology has its drawbacks.
In this new download, “The Advantages of Virtual Barrier Video Analytics,” you’ll learn how new video analytics technology overcomes the challenges of traditional motion-based analytics.
CNN (convolutional neural networks) video analytics reduces false alarms, supports geo-location of targets, and can detect and classify objects in frame, whether they are moving or not.
Download your free copy today and see a comparison between motion-based and CNN analytics. And learn how this emerging technology enhances threat detection, delivers greater accuracy and critical situational awareness when tracking and responding to intruders, while minimizing false alarms.