AI system finds and predicts criminal patterns in vast troves of surveillance footage
Security organisations can’t keep up with the sheer volume of surveillance footage available today. The machine-learning based SURVANT system sifts through the data to find patterns of crimes and even predicts their evolution.
Security organisations and agencies around the world deploy increasing amounts of video surveillance to monitor and protect people, property and public infrastructure. The amount of available footage is exploding, thanks to growing numbers of cameras operating at higher resolutions, making it difficult for human surveillance teams to analyze all of the footage – especially if they have other tasks.
Automated surveillance could help but requires advanced analytics to be effective in fighting crime. Many organizations have invested heavily in surveillance systems and are keen to exploit the video footage they have collected to this end. The EU-funded SURVANT project created a system to do exactly this.
“SURVANT addresses system scalability issues that emerge from the explosion in the amount of available video content,” says Mr Giuseppe Vella, SURVANT project coordinator. SURVANT analyses relevant surveillance videos to extract inter/intra-camera video analytics, before enriching this information with reasoning and inference. It then assists investigators to search efficiently and effectively through video archives, to find critical elements of criminal activity among the sea of footage.
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