With iCetana your operator now has the ability to monitor approximately 600 cameras. That’s a 3750% rise in monitoring capability, improving operator engagement and efficiency.
This means that a pair of operators with two workstations have been able to do the job previously requiring a room full of personnel and computers, thereby dramatically improving control room efficiency.
♦ Operator fatigue is a real issue – studies show that most operators miss a large proportion of interesting activity if they are watching a camera for more than 20 minutes. There is also a massive data overload – far more cameras exist out there in the world than we have people able to look at them. How do you solve this? You need something to filter out the noise.
♦ Traditional video analytics are not very scalable. You need something that is simple to integrate, and provides real-time alerts. ICE co-exists with existing solutions to improve productivity.
ICE’s patented software is a spin-out from Curtin University and learns from existing footage of cameras and reports anomalous activity in real-time. e.g. people sprinting across the street, people partaking in graffiti, fires breaking out at 2:30am being detected or even people falling down stairs in an unfortunate accident. The potential applications are very diverse.
https://en.wikipedia.org/wiki/Svetha_Venkatesh
Venkatesh has developed new technologies in large-scale pattern recognition in big data.[4]
Her work has led to start-ups such as iCetana which finds anomalies through video analytics to detect potential security threats in large data sets;[4] the development of a health analytics program which enables doctors to predict suicide risk;[5] and PRaDA’s development of the Toby Playpad app which provides therapy for children with autism.[6][7]
Her work on using surveillance data led to the development of a “virtual observer” which was used after the 2005 London bombings.[8]
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