VISION-BASED ONLINE MULTI-STREAM BEHAVIOR DETECTION APPLYING BAYESIAN NETWORKS (FriAmPO1)

Author(s) : Dejan Arsic (Munich University of Technology, Germany) Frank Wallhoff (Munich University of Technology, Germany) Björn Schuller (Munich University of Technology, Germany) Gerhard Rigoll (Munich University of Technology, Germany)

Abstract : In the present treatise, we propose an approach for a highly configurable image based online person behavior monitoring system. The particular application scenario is a crew supporting multi-stream on-board threat detection system, which is getting more desirable for the use in public transport. For such frameworks, to work robust in mostly unconstrained environments, many subsystems have to be employed. Although the research field of pattern recognition has brought up reliable approaches for several involved subtasks in the last decade, there often exists a gap between reliability and the needed computational efforts. However, to reach this highly demanding task, several straight forward technologies, here the output of several so-called weak classifiers using low-level features are fused by a sophisticated Bayesian Network

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