Automatic behavior understanding in crisis response control rooms

Abstract

This paper addresses the problem of automatic behavior understanding in smart environments. Automatic behavior understanding is defined as the generation of semantic event descriptions from machine perception. Outputs from available perception modalities can be fused into a world model with a single spatiotemporal reference frame. The fused world model can then be used as input by a reasoning engine that generates semantic event descriptions. We use a newly developed annotation tool to generate hypothetical machine perception outputs instead. The applied reasoning engine is based on fuzzy metric temporal logic (FMTL) and situation graph trees (SGTs), promising and universally applicable tools for automatic behavior understanding. The presented case study is automatic behavior report generation for staff training purposes in crisis response control rooms. Various group formations and interaction patterns are deduced from person tracks, object information, and information about gestures, body pose, and speech activity.