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

Journal Article


Abstract


  • IEEE 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.

UOW Authors


  •   Li, Shuai (external author)
  •   Florencio, Dinei (external author)
  •   Li, Wanqing
  •   Zhao, Yaqin (external author)
  •   Cook, Christopher

Publication Date


  • 2018

Citation


  • Li, S., Florencio, D., Li, W., Zhao, Y. & Cook, C. (2018). A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain. IEEE Transactions on Image Processing, 27 (8), 3918-3930.

Scopus Eid


  • 2-s2.0-85045755274

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1426

Number Of Pages


  • 12

Start Page


  • 3918

End Page


  • 3930

Volume


  • 27

Issue


  • 8

Place Of Publication


  • United States

Abstract


  • IEEE 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.

UOW Authors


  •   Li, Shuai (external author)
  •   Florencio, Dinei (external author)
  •   Li, Wanqing
  •   Zhao, Yaqin (external author)
  •   Cook, Christopher

Publication Date


  • 2018

Citation


  • Li, S., Florencio, D., Li, W., Zhao, Y. & Cook, C. (2018). A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain. IEEE Transactions on Image Processing, 27 (8), 3918-3930.

Scopus Eid


  • 2-s2.0-85045755274

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1426

Number Of Pages


  • 12

Start Page


  • 3918

End Page


  • 3930

Volume


  • 27

Issue


  • 8

Place Of Publication


  • United States