Suspicious Behavior Detection in Public Transport by Fusion of Low-Level Video Descriptors

Great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passengers´ 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 in an airplane situation show promising results.