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Video applications usually involve a large number of moving objects. Moving objects refer to semantic realworld entities denoting a coherent spatial region and being automatically computed by the continuity of spatial and temporal low-level features, such as color and motion. In surveillance application, spatial and temporal relationships among these objects should be efficiently supported and retrieved...
In this paper, we propose a two-level integrated model for accurate 3D tracking of rigid head motion and non-rigid facial animation. At the lowest level, the 2D shape of facial features is robustly extracted using a regularized shape model and a cascade multi-stage algorithm. At the highest level, we estimate both the facial animation and 3D pose parameters via minimizing an energy function comprising...
This paper presents a Bayesian approach for simultaneously detecting the moving objects (foregrounds) and estimating their motion in image sequences taken with a moving camera mounted on the top of a mobile robot. To model the background, the algorithm uses the GMM approach for its simplicity and capability to adapt to illumination changes and small motions in the scene. To overcome the limitations...
In this paper we propose a method of representing knowledge structures for visual content categorization and annotation in video surveillance applications. In order to achieve this purpose, we designed and implemented an ontology database for storing templates of domain-dependent moving objects and scene regions descriptions. The novelty of the system consists in the employment of a double layer,...
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