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In this paper we develop a first step towards the recognition of hand activity by detecting objects subject to manipulation, and use the results to build a visual summary of events. The motivation is to extract information from hand activity without requiring that the wearer is explicit as in gesture-based interaction. Our method uses simple image measurements within a probabilistic framework and allows real-time implementation.

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This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the localization task. Omnidirectional images are used to create the compact model, as well as to estimate the robot position within the environment. These images are characterized through global appearance descriptors, since they constitute a straightforward mechanism to build a compact model and estimate the robot position. To evaluate the goodness of the proposed clustering algorithms, several datasets are considered. They are composed of either panoramic or omnidirectional images captured in several environments, under real operating conditions. The results confirm that compression of visual information contributes to a more efficient localization process through saving computation time and keeping a relatively good accuracy.