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- W2022954174 abstract "We propose a tracking-free method to detect the regions of interest (ROI) in a wide-angle video stream. A region is defined as a statistical outlier among occurrences of motion patterns, and is detected in an unsupervised manner. Based on 3D structure tensors, the activity at any site is modeled by the probability distribution of distances between structure tensors. The distribution is estimated using a nonparametric kernel density estimator. The detection of regions is determined by observing a long period of low-probability motion occurrences. Experiments performed with real-world datasets indicate that the proposed algorithm can detect both spatial ROIs and spatio-temporal ROIs, and outperforms other nonparametric methods." @default.
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- W2022954174 date "2014-11-01" @default.
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- W2022954174 title "A nonparametric approach to region-of-interest detection in wide-angle views" @default.
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- W2022954174 doi "https://doi.org/10.1016/j.patrec.2014.05.019" @default.
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