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

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 under-detectio...

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-08, Vol.27 (8), p.3918-3930
Main Authors: Shuai Li, Florencio, Dinei, Wanqing Li, Yaqin Zhao, Cook, Chris
Format: Article
Language:English
Subjects:
background subtraction
camouflaged foreground
Computational modeling
Foreground detection
Image color analysis
Image edge detection
Kernel
Wavelet domain
wavelet transform
Wavelet transforms
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Summary: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 under-detection 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 with 0.71 to 0.8 for the other state-of-the-art methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2018.2828329