Audiovisual Behavior Modeling by Combined Feature Spaces

Abstract

Great interest is recently shown in behavior modeling, especially in public surveillance tasks. In general it is agreed upon the benefits of use of several input cues as audio and video. Yet, synchronization and fusion of these information sources remains the main challenge. We therefore show results for a feature space combination, which allows for overall feature space optimization. Audio and video features are thereby firstly derived as low-level-descriptors. Synchronization and feature combination is achieved by multivariate time-series analysis. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral, and tired behavior in an airplane situation show a significant improvement over each single modality.