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In many computer vision tasks, scene changes hinder the generalization ability of trained classifiers. For instance, a human detector trained with one set of images is unlikely to perform well in different scene conditions. In this paper, we propose an incremental learning method for human detection that can take generic training data and build a new classifier adapted to the new deployment scene...
Human detection and recognition at a distance is recently a matter of great concern among computer vision researchers. This paper introduces a new set of human body features for the recognition of detected human as an object. The feature extraction is performed by an established human model consisting of five parts. These features consist of geometric calculations of detected object and their different...
We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a static setting with significant human input. To remove these limitations, we propose a more general semi-supervised learning technique to tackle the problem. First, we exploit characteristic differences in color and edges in...
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