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A semantic concept for the mapping of low-level analysis data to high-level scene descriptions

Along with the growing need for security, an increasing amount of surveillance content is created. It is indispensable to index the content in advance in order to enable quick and reliable searches on the output of hundreds or thousands of surveillance sensors installed at a single facility. For this purpose, the concept of Smart Indexing and Retrieval (SIR) enables cost efficient searches by generating high-level meta data. The generation of this meta data has to be done automatically, based on the low-level features extracted by content analysis algorithms. Creating it manually becomes more and more difficult to handle in reasonable time at reasonable costs. Whereas formerly proposed approaches have been strongly application dependent, in this thesis, a generic concept for mapping the results of the lowlevel content analysis data to semantic event descriptions and its application is presented. The constituting elements of this approach and their underlying concepts as well as an introduction to their application are shown. The main contribution of the approach are the generality and the early stage at which the step from low-level to high-level representation is taken. This reasoning in the meta data domain is performed on small time frames while the reasoning on complexer scenes is done in the semantic space. Even an unsupervised self-assessment is possible using the semantic approach.