Research in the surveillance domain was confined for years in the military domain. Recently, as military spending for this kind of research was reduced and the technology matured, the attention of the research and development community turned to commercial applications of surveillance. In this paper we describe a state-of-the-art monitoring system developed by a corporate R&D lab in cooperation with the corresponding security business units. It represents a sizable effort to transfer some of the best results produced by computer vision research into a viable commercial product. Our description spans both practical and technical issues. From the practical point of view we analyze the state of the commercial security market, typical cultural differences between the research team and the business team and the perspective of the potential users of the technology. These are important issues that have to be dealt with or the surveillance technology will remain in the lab for a long time. From the technical point of view we analyze our algorithmic and implementation choices. We describe the improvements we introduced to the original algorithms reported in the literature in response to some problems that arose during field testing. We also provide extensive experimental results that highlight the strong points and some weaknesses of the prototype system.

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We show how the Koho,nen self-organizing feature map model can be extended so that partial training data can be utilized. Given input stimuli in which values for some elements 01 Features are absent, the match computation and the weight updates are performed in lhe input subspace defined by the available input values. Three examples, including an application to student modelling for intelligent tutoring syslems in which data is inherently incomplete, demonstrate the effectiveness of the extension. s~z e expeiimen:a! iesa!:~ oii :hie problems. In one of these problems-the development of student models for use in intelligent tutoring systems-the data is inherently incomplete. We conclude with some suggestions for future work. The Kohonen self-organizing feature mapThe Kohonen self-organizing feature map (Kohonen 1984) is an unsupervised learning network that preserves topological information about the input space. The map is 0954-898Xp2p7.0205 +08$04.50