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The Devices for Assisted Living(DALi ) project is a research initiative sponsored by the European Commission under the FP7 programme aiming for the development of a robotic device to assist people with cognitive impairments in navigating complex environments. The project revisits the popular paradigm of the walker enriching it with sensing abilities (to perceive the environment), with cognitive abilities (to decide the best path across the space) and with mechanical, visual, acoustic and haptic guidance devices (to guide the person along the path). In this paper, we offer an overview of the developed system and describe in detail some of its most important technological aspects

Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass [5] more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincáre-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.

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Fingerprint recognition and .verification are often based on local fingerprint features, usually ridge endings or terminations, also called minutiae. By exploiting the structural uniqueness of the image region around a minutia, the fingerprint recognition performance can be significantly enhanced.However, for most fingerprint images the number of minutia image regions (MIR's) becomes dramatically large, which imposes -especially for embedded systems -an enormous memory requirement. Therefore, we are investigating different algorithms for compression of minutia regions. The requirement for these algorithms is to achieve a high compression rate (about 20) with minimum loss in the matching performance of minutia image region matching. In this paper we investigate the matching performance for compression algorithms based on the Principal Component and the wavelet transformation. The matching results are presented in form of normalized ROC curves and interpreted in terms of compression rates and the MIR dimension.

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Existing visual surveillance systems typically require that human operators observe video streams from different cameras, which becomes infeasible if the number of observed cameras is ever increasing. In this paper, we present a new surveillance system that combines automatic video analysis (i.e., single person tracking and crowd analysis) and interactive visualization. Our novel visualization takes advantage of a high resolution display and given 3D information to focus the operator's attention to interesting/ critical areas of the observed area. This is realized by embedding the results of automatic scene analysis techniques into the visualization. By providing different visualization modes, the user can easily switch between the different modes and can select the mode which provides most information. The system is demonstrated for a real setup on a university campus

Unusual event detection, i.e., identifying unspecified rare/critical events, has become one of the major challenges in visual surveillance. The main solution for this problem is to describe local or global normalness and to report events that do not fit to the estimated models. The majority of existing approaches, however, is limited to a single description (e.g., either appearance or motion) and/or builds on inflexible (unsupervised) learning techniques, both clearly degrading the practical applicability. To overcome these limitations, we demonstrate a system that is capable of extracting and modeling several representations in parallel, while in addition allows for user interaction within a continuous learning setup. Novel yet intuitive concepts of result visualization and user interaction will be presented that allow for exploiting the underlying data.