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

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 in an airplane situation show promising results.

• Type: Conference
• Year: 2007
• Where: ICMCS
• Authors: Dejan Arsic, Björn Schuller, Gerhard Rigoll
• Keywords: Global Motion Features, ICMCS 2007, Public Transportation Systems, Visual Low-level Features