From Distributed Vision Networks to Human Behavior Interpretation

Abstract. Analysing human behavior is a key step in smart home applications. Many reasoning approaches utilize information of location and posture of the occupant in qualitative assessment of the user’s status and events. In this paper, we propose a vision-based framework to provide quantitative information of the user’s posture which can be used to deduct qualitative representations for high-level reasoning. Furthermore, our approach is motivated by potentials introduced by interactions between the vision module and the high-level reasoning module. While quantitative knowledge from the vision network can either complement or provide specific qualitative distinctions for AI-based problems, these qualitative representations can offer clues to direct the vision network to adjust its processing operation according to the interpretation state. The paper outlines potentials for such interactions and describes two visionbased fusion mechanisms. The first employs an opportunistic approach to recover the full-parameterized human model by the vision network, while the second employs directed deductions from vision to address a particular smart home application in fall detection.