A suitcase lies abandoned in a busy airport terminal. Was it planted by a bomber, or carelessly left for a couple of minutes while the owner went to buy coffee?

One of the most common headaches facing security staff may soon be remedied with the help of "intelligent security cameras" developed by European scientists.

A newly concluded research project relies on formulae known as algorithms to enable computers to analyze video images and spot potential threats, from abandoned baggage to people loitering suspiciously.

"It's (about) developing solutions so that computers can detect abnormal behavior," said project coordinator Jean-Marc Suchier of French group Sagem Defense Securite, a unit of Safran.

For security staff at airports, often monitoring images from dozens of surveillance cameras at once, the new technology offers the promise of picking out dangers that might otherwise be missed.

"The idea is to automatically analyze and intelligently filter all of that video, but also to add a next level of intelligence," said James Ferryman, a specialist in "computational vision" at the University of Reading in England.

"We're talking about smart cameras which go to the next level of proactive detection."

Mainly funded by the European Union, the two-year, EUR2.3 million (US$3.11 million) project involved 10 European companies and research institutes and is known as ISCAPS (Integrated Surveillance of Crowded Areas for Public Security).

It focused on several key threats: abandoned bags; erratic movements or loitering by individuals; suspicious vehicle movements; and "drop dead scenarios" in which people fall to the ground, possibly affected by smoke or some kind of attack.

To define "suspicious behavior", the researchers conducted extensive interviews with security experts. They used actors to play out scenarios and mapped these into computer algorithms.

They set up cameras to monitor public areas over weeks or months, enabling the system to build statistical models showing busy and slack times and typical patterns of movement - and then to spot anything that diverges from the pattern.

The scientists had to battle a host of technical challenges, including variable weather and lighting and the need to quickly analyse dense, complex images of people and objects.

They stressed that while the system can flag something suspicious, it will still fall to a human operator to make the final call - for example, whether someone is running for a train or sprinting to escape the scene of a crime.