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In this paper, we propose a background subtraction (BGS) method based on the Gaussian mixture models using color and depth information. For combining color and depth information, we used the probabilistic model based on Gaussian distribution. In particular, we focused on solving color camouflage problem and depth denoising. For evaluating our method, we(More)
In this paper, we present a new practical background subtraction method taking advantages of the conventional codebook and GMM-based approaches. The fundamental idea is approximating GMM parameters based on color statistics of background pixels which are clustered by the computationally efficient codebook scheme. The experiments on real visual surveillance(More)
In traffic monitoring systems, it is very important to train an accurate vehicle image-classifier to implement automated video analysis techniques such as detection and tracking. In general, classifiers are obtained from manually collected and labeled training sample images. However, this approach has a problem that it requires significant human efforts to(More)
In highway traffic monitoring systems, vehicle detection is one of the most important tasks. To automatically detect vehicles in general CCTV environments, we should effectively handle the problems caused by the following three issues: severe variation in appearance, ambiguities in location, and ambiguities in size. Over the last decade, these issues have(More)
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