Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Rule-based high-level situation recognition from incomplete tracking data

2012 , Münch, David , Ijsselmuiden, Joris , Grosselfinger, Ann-Kristin , Arens, Michael , Stiefelhagen, Rainer

Fuzzy metric temporal logic (FMTL) and situation graph trees (SGTs) have been shown to be promising tools in high-level situation recognition. They generate semantic descriptions from numeric perceptual data. FMTL and SGTs allow for sophisticated and universally applicable rule-based expert systems. Dealing with incomplete data is still a challenging task for rule-based systems. The FMTL/SGT system is extended by interpolation and hallucination to become capable of incomplete data. Therefore, one analysis to the robustness of the FMTL/SGT system in situation recognition is removing parts of the ground truth input tracks. The recognition results are compared to ground truth for situations such as "load object into car". The results show that the presented approach is robust against incomplete data. The contribution of this work is, first, an extension to the FMTL/SGT system to handle incomplete data via interpolation and hallucination, second, a knowledge base for recognizing vehicle-centered situations.

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Automatic behavior understanding in crisis response control rooms

2012 , Ijsselmuiden, Joris , Grosselfinger, Ann-Kristin , Münch, David , Arens, Michael , Stiefelhagen, Rainer

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.

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Towards a multi-purpose monocular vision-based high-level situation awareness system

2011 , Münch, David , Jüngling, Kai , Arens, Michael

In surveillance applications human operators are either confronted with a high cognitive load or monotonic time periods where the operator's attention rapidly decreases. Therefore, automatic high-level interpretation of image sequences gains increasing importance in assisting human operators. We present a generic hierarchical system that generates high-level logic-based situation descriptions in various domains. The system consists of two components. First, a vision component provides 3D spatial and temporal information about objects in scenes. Second, the situation recognition component uses knowledge encoded in Situation Graph Trees and a fuzzy graph traversal allowing exhaustive situation awareness. The system is tested with real video data comprising persons, their actions, and interactions. In order to show the domain independence we used recorded data from moving vehicles and static surveillance cameras. The results show that the system is usable with multi modal data and can easily be modified and extended.

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