The Pattern Recognition (PR) and Artificial Intelligence (AI) scientific communities have shared knowledge and effort in order to obtain more effective solutions for many different research areas. However, although the techniques and approaches are somewhat similar, the two communities often tackle problems from rather different perspectives.
Among these research areas, Human Behaviour Analysis (HBA) has recently become a very popular topic in computer science, because of its relevance to surveillance. For instance, with the increasing use of cameras for video surveillance, it is almost unfeasible for security personnel to monitor scenes or to watch recorded videos looking for a particular event or visual item. Automatic interpretation of actions, gestures, and interactions among people, as well as automatic situation recognition and assessment, would be extremely useful. Actually, methods for HBA have this specific aim, and they are attracting more and more researchers and Information and Communication Technologies (ICT) industries. Nevertheless, the recognition and interpretation of human behaviours are still challenging problems.
- This Special Section follows from a very successful workshop, the first Workshop on Pattern Recognition and Artificial Intelligence for Human Behaviour Analysis (PRAI*HBA – http://imagelab.ing.unimore.it/prai4hba/) held in Reggio Emilia (Italy) on 12 December 2009 during the AI*IA 2009 Conference. It contains two articles that are extended versions of two of the best papers presented at the workshop and contains notable contributions to HBA from the PR and the AI perspectives
In the first paper ‘Social Interactions by Visual Focus of Attention in a Three-Dimensional Environment', by Bazzani, Tosato, Cristani, Farenzena, Paggetti, Menegaz and Murino, a novel approach to social interaction discovery is presented; instead of using global or local appearance features, the authors exploit the Subjective View Frustum, which approximates the visual field of a person in a three-dimensional representation of the scene.
The main contribution of the second paper 'Human action recognition using an ensemble of body-part detectors', by Chakraborty, Bagdanov, Gonzalez and Roca, is to transform the problem of action recognition into that of recognising the distinctive motion of specific body parts, for instance, the legs for walking, the hands for boxing, etc. The intuition behind the approach is that several human actions can be described more compactly and effectively by considering only the relevant motions of the body parts actually performing the actions.
We hope you enjoy the special section.