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An appearance-based approach to track an object that may undergo appearance change is proposed. Unlike recent methods that store a detailed representation of object's appearance, this method allows an appearance feature with a reduced dimension to be used. Through the use of a sparse Bayesian classifier, high classification and detection accuracy can be maintained even if a reduced feature vector...
In this contribution maximum likelihood (ML) based approaches are presented which track an a-priori known surface and texture in monocular video streams. In contrast to established tracking algorithms based on homographies the surface is not modeled as planar or piecewise planar but as a collection of 3D surface points and surface normals. Thus, any free-form surface can be modeled. This paper introduces...
Tracking people or objects across multiple cameras is a challenging research area in visual computing especially when these cameras have non-overlapping field-of-views. The important task is to associate a target of interest with its previous appearances across time and space within the camera network. In this paper, we propose a unified tracking framework using particle filter to efficiently switch...
The problem of detecting and locating point targets in cluttered and noised IR (infrared) images is a difficult and important task in IR applications. In this paper, we proposed a new method for detecting dim moving point target in IR image sequence. The significance of this work is that the ideal of bilateral filtering is adopted, which leads to a simple detection. In order to make use of the temporal...
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