A hierarchical approach for visual suspicious behavior detection in aircrafts

Recently great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passenger´s behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with support vector machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired behavior.