Computer tool spots aggressive human behaviour in CCTV images

Researchers have developed a new computer programme that can analyse closed circuit television (CCTV) images and spot aggressive human behaviour with 90 per cent accuracy.
The research is an important step forward in intelligent security systems that could raise an alarm without requiring constant human vigilance, researchers said.
Image-processing experts Abdelhak Ouanane and Amina Serir of the Universite des Sciences et de la Technologie Houari Boumediene in Algiers, Algeria, used a geometrical analysis of images to create a silhouette of a person on the screen.
The system then maps the movements of the person's limbs, the team then correlates those movements with aggressive and passive behaviour so that the algorithm learns what particular changes in geometry are associated with aggression.

The programme can automatically distinguish between hand clapping, waving and a punch being thrown, for instance. The system can also discern whether a person is walking, jogging or running.
The resulting algorithm has 90 per cent accuracy, compared with other systems the best of which is around 80 per cent accurate. On a standard data set the accuracy is as high as 98 per cent whereas the best alternative is 95 per cent.

The team pointed out that the algorithm is robust and not susceptible to changes in lighting conditions and noise in the images.
This allows it to work well in a variety of indoor and outdoor settings, street, airport, sports stadium etc.

Moreover, the simplification of the images to human silhouettes reduces the computational overhead significantly and allows the analysis to be carried out quickly without the need for a high-performance computer.
With increasing numbers of CCTV cameras monitoring people in city centres as part of crime-reduction efforts, technology that can automate the process of spotting aggressive behaviour without increasing numbers of people to monitor the video streams is becoming more and more important, researchers said.
The study is published in the International Journal of Computational Vision and Robotics.