See beyond
Introducing IntelexVision
Headquartered in the United Kingdom, with offices in Europe, Australia, Middle East, and South Africa, IntelexVision delivers a complete, end-to-end solution, enabling effective real-time monitoring of video at scale. Our analytics autonomously monitor and interpret massive amounts of video footage in an unbiased, unsupervised way, using AI to ‘understand’ and learn from any monitored scene, generating critical triggers that alert operators to real and ongoing potential risks. Security control centres are empowered to take meaningful action and respond to threats in real time.
Our team are highly skilled with deep knowledge and expertise in AI, video surveillance and software integration including many years experience in the CCTV industry.
Our vision
The world faces increasing and unpredictable security threats. As governments and the private sector respond the total number of CCTV cameras globally has grown to over 1 billion and that number is increasing exponentially.
More cameras, however, does not necessarily result in enhanced security. As CCTV cameras proliferate, trillions of petabytes of information and images are generated. To be useful, these would require 24/7 attention, but no number of control centre operators can successfully watch and monitor this vast amount of live video. The key to improved safety and security is therefore managing risks and threats with technology that enables real-time video analysis.
Over the past decade, multiple video analytics providers have launched solutions that can address individual parts of the overall problem. The core challenge, however, remains unresolved. With the currently available solutions in the real-time video analysis market, control centres cannot respond adequately to most security threats as they happen.
The IntelexVision iSentry platform: From AI to action
iSentry is our full spectrum AI analysis platform for real-time monitoring of video surveillance imagery focused on dealing with a wide range of complex, live video environments.
Our unsupervised machine learning networks extract relevant data from imagery through behavioural anomaly detection (Unusual Behaviour) in fluid, busy dynamic environments, or advanced motion analysis (TREX) in more static surroundings where intrusion detection is required. These alerts are then classified for better contextualization and sent to a powerful rules engine which automates part of the decision-making process.
With thousands of hours of video distilled into clips lasting just a few seconds, security control centres can dramatically improve their efficiency, while at the same time significantly reducing the number of false-positive and false-negative alerts. It also results in much lower hardware investment costs and lower human resource requirements.
