In our society with its aging population, the design and implementation of a highperformance distributed multi-sensor and information system for autonomous physical services become more and more important. In line with this, this thesis proposes an Intelligent Multi-Sensor System, IMSS, that surveys a human activities space to detect and identify a target for a specific service. The subject of this thesis covers three main aspects related to the set-up of an IMSS: an improved depth measurement and reconstruction method and its related uncertainty, a surveillance and tracking algorithm and finally a way to validate and evaluate the proposed methods and algorithms. The thesis discusses how a model of the depth spatial quantisation uncertainty can be implemented to optimize the configuration of a sensor system to capture information of the target objects and their environment with required specifications. The thesis introduces the dithering algorithm which significantly reduces the depth reconstruction uncertainty. Furthermore, the dithering algorithm is implemented on a sensor-shifted stereo camera, thus simplifying depth reconstruction without compromising the common stereo field of view. To track multiple targets continuously, the Gaussian Mixture Probability Hypothesis Density, GM-PHD, algorithm is implemented with the help of vision and Radio Frequency Identification, RFID, technologies. The performance of the tracking algorithm in a vision system is evaluated by a circular motion test signal. The thesis introduces constraints to the target space, the stereo pair characteristics and the depth reconstruction accuracy to optimize the vision system and to control the performance of surveillance and 3D reconstruction through integer linear programming. The human being within the activity space is modelled as a tetrahedron, and a field of view in spherical coordinates are used in the control algorithms. In order to integrate human behaviour and perception into a technical system, the proposed adaptive measurement method makes use of the Fuzzily Defined Variable, FDV. The FDV approach enables an estimation of the quality index based on qualitative and quantitative factors for image quality evaluation using a neural network. The thesis consists of two parts, where Part I gives an overview of the applied theory and research methods used, and Part II comprises the eight papers included in the thesis.