IDIS has launched its AI in the Box (DV-2116), boosting the power of surveillance systems with “the most accurate deep learning analytics yet developed”. During independent tests, IDIS Deep Learning Analytics (IDLA) has achieved accuracy rates of 97%, a record performance further boosted by high-speed processing.
The DV-2116 makes deep learning analytics more affordable, enhancing security and Control Room efficiency. The plug-and-play IDLA-ready appliance comes embedded with an NVDIA GTX1060 GPU chipset allowing the analysis of up to 16 channels simultaneously.
End users benefit from robust and calibration-free object detection and classification (objects such as people, cars and bicycles), intrusion and loitering detection, powerful and intelligent search functions and tracking by colour, object and number.
The introduction of AI in the Box makes deep learning analytics easier to adopt through trouble-free plug-and-play installation via the IDIS Solutions Suite video management software. This allows installation without costly disruption. The 97% accuracy minimises false alarms, in turn improving detection and monitoring performance.
James Min, managing director of IDIS Europe, commented: “Our high accuracy analytics can process vast amounts of data without a break in a way that human operators cannot. This means that high-resolution video streams can be automatically monitored to spot suspicious behaviour or distinguish potential threats from everyday activity.”
IDIS’ Deep Learning Engine, which powers the new solution, can recognise potentially significant movements and the characteristics of people and vehicles, while ignoring activity that isn’t relevant. The technology can quickly check through hours of video to find specific individuals. It also becomes more accurate over time due to its self-learning characteristics.
Min concluded: “This is very exciting as it means that time-critical activities, such as investigating incidents, will become increasingly efficient as our analytics are embedded in operations.”