Adaptive Motion Segmentation for Changing Background

Guan, Yuanyuan

doi:10.4236/jsea.2009.22014

Cited by 2 publications

(1 citation statement)

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“…The region connectivity with chromatic consistency is further used in the background update to overcome the aperture problem in [66]. This method was further improved in [67], where the RGB color space is used and the foreground segmentation is further developed based on the motion variation and chromatic characteristics. In [68], [69], a color ratio difference was further adopted to suppress shadow.…”

Section: B Transform Domain Background Modelsmentioning

confidence: 99%

A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

Florêncio

et al. 2018

IEEE Trans. on Image Process.

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Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from underdetection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87, compared to 0.71 to 0.8 for the other stateof- the-art methods.

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