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.
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.
Action recognition in surveillance systems has to work 24/7 under all kinds of weather and lighting conditions. Towards this end, most action recognition systems only work in the visible spectrum which limits their general usage to daytime applications. In this work Hough forests are applied to the longwave infrared spectrum which can capture humans both in the dark and in daylight. Further, Integral Channel Features which have shown promising results in the spatial domain are applied to the spatio-temporal domain and are incorporated into the Hough forest approach. This approach is evaluated on a new outdoor dataset containing different violent and non-violent actions recorded in the visible and infrared spectrum. It is further shown that for the visible spectrum the proposed approach achieves state-of-the-art results on the KTH and i3DPost dataset.
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