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Foreground detection is a fundamental step in visual surveillance. However, accurate foreground detection is still a challenging task especially in dynamic backgrounds. In this paper, we present a nonparametric approach to foreground detection in dynamic backgrounds. It uses a history of recently pixel values to estimate background model. Besides, the adaptive threshold and spatial coherence are introduced...
We cast vehicle recognition as problem of feature representation and classification, and introduce a sparse learning based framework for vehicle recognition and classification in this paper. After objects captured with a GMM background subtraction program, images are labeled with vehicle type for dictionary learning and decompose the images with sparse coding (SC), a linear SVM trained with the SC...
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