A Multi Sensor System for a Human Activities Space : Aspects of Planning and Quality Measurement

In our aging society, the design and implementation of a high-performance autonomous distributed vision information system for autonomous physical services become ever more important. In line with this development, the proposed Intelligent Vision Agent System, IVAS, is able to automatically detect and identify a target for a specific task by surveying a human activities space. The main subject of this thesis is the optimal configuration of a sensor system meant to capture the target objects and their environment within certain required specifications. The thesis thus discusses how a discrete sensor causes a depth spatial quantisation uncertainty, which significantly contributes to the 3D depth reconstruction accuracy. For a sensor stereo pair, the quantisation uncertainty is represented by the intervals between the iso-disparity surfaces. A mathematical geometry model is then proposed to analyse the iso-disparity surfaces and optimise the sensors’ configurations according to the required constrains. The thesis also introduces the dithering algorithm which significantly reduces the depth reconstruction uncertainty. This algorithm assures high depth reconstruction accuracy from a few images captured by low-resolution sensors. To ensure the visibility needed for surveillance, tracking, and 3D reconstruction, the thesis introduces constraints of the target space, the stereo pair characteristics, and the depth reconstruction accuracy. The target space, the space in which human activity takes place, is modelled as a tetrahedron, and a field of view in spherical coordinates is proposed. The minimum number of stereo pairs necessary to cover the entire target space and the arrangement of the stereo pairs’ movement is optimised through integer linear programming. In order to better understand human behaviour and perception, the proposed adaptive measurement method makes use of a fuzzily defined variable, FDV. The FDV approach enables an estimation of a quality index based on qualitative and quantitative factors. The suggested method uses a neural network as a tool that contains a learning function that allows the integration of the human factor into a quantitative quality index. The thesis consists of two parts, where Part I gives a brief overview of the applied theory and research methods used, and Part II contains the five papers included in the thesis