Tool to help CCTVs peer into blind spots

It has been developed by the Queen Mary, University of London,

Says Andrea Cavallaro, professor at Queen Mary's Centre for Intelligent Sensing and study co-author: "Linking distant and disjointed camera views to follow individuals in a large CCTV network, for example in a train (sic) station or in a sports venue, enhances the ability to monitor wide areas to tackle crime."

"Also, this new research model could be used to collect data to guide the redesign of the layout of buildings in order to facilitate the flow of people, which could help evacuation in an emergency situation," Cavallaro has been quoted as saying by the journal Neurocomputing.

Researchers created a novel re-identification method that predicts a person's movements in invisible areas using a combo of behavioural models and floor plans.

The model was tested using CCTV footage from London's Gatwick airport to predict a person's movements based on specific destinations on site such as exits, shops, seating areas and meeting points.

The possible path each person is likely to follow is predicted after generating a number of potential movement trajectories from one monitored zone to another, using the fact that specific destinations act as 'attractors' for human movements.

The model accounts for the natural willingness of people to stay at a comfortable distance from walls and other barriers, according to a Queen Mary statement.

The research will feed into a new EU four-year video surveillance project called CENTAUR, coordinated by Fortune 100 company Honeywell.