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In this paper, we present a coordinated video surveillance system that can minimize the spatial limitation and can precisely extract the 3D position of objects. To do this, our system used an agent based system and also tracked the normalized object using active wide-baseline stereo method.The system is composed of two parts: multiple camera agents (CAs) and a support module (SM). Each CA treats image processing and camera controlling. A SM performs a role that manages communication between CAs. Our proposed system extracts object positions independent of environment via the collaboration of CAs and a SM. Finally, through experimental results we show that the proposed system successfully tracks an object on real-time.

In an environment where the contexts of users are complex and the degree of freedom of user activity is very high, such as in daily life, several factors need to be considered for constructing user models. Such a model should include changes in the meanings of activities that reflect the user's situation both temporally and individually. In this paper we propose a novel approach for personalizing the user model and adapting it to individual circumstances with a wearable sensor network. We also describe the process for determining the repetitive activities of a user by using incremental clustering and Bayesian network. We show experimental results for an adaptive user model based on a real wearable sensor platform. Multimedia data of user experience are acquired from the multimodal sensors, and processed to metadata that have meanings.

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