Washington, Sep 19 (ANI): An intelligent surveillance system, developed by researchers from the University of Castilla-La Mancha (UCLM), can detect aberrant behaviour by drivers and people on foot crossing pedestrian crossings and in other urban settings.
The study, by David Vallejo, from the ORETO Applied Intelligent Systems research group of the UCLM, could be used to penalise incorrect behaviour.
"We have developed an intelligence surveillance software and related theoretical model in order to define 'normality' in any setting one wishes to monitor, such as a traffic scenario", said Vallejo.
The study focused on a pedestrian crossing in a two-way street, regulated by a traffic Light.
The authors defined 'normal' behaviour of cars and pedestrians in this setting, in which they can move when the lights are green, but must stop and not cross the safety lines when the lights are red.
The system, which works in a similar way to a human monitor, can detect whether the vehicles and pedestrians are moving "normally".
If at any point any of the movements related to these "objects" is not 'normal' (driving through a red light, for example), the programme recognizes that the behaviour differs from the normal framework established.
The supporting architecture underlying the model is a multi-agent artificial intelligence system (made up of software agents that carry out the various tasks) involved in monitoring the environment.
To prove the effectiveness of the model, its creators have developed a monitoring tool (OCULUS), which analyses images taken from a real setting.
For this, the team members placed a video camera close to their place of work, the Higher School of Information Technology in Ciudad Real.
"In this way we are able to identify any drivers and pedestrians behaving abnormally, meaning the programme could be used in order to penalise such behaviours", said Vallejo.
Currently, the researchers are continuing their work to fine tune the system, and believe it will be possible to use it in future in other situations, for example in analysing behaviour within indoor environments (museums, for example), or in detecting overcrowding.he study has been published in the journal Expert Systems with Applications. (ANI)