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

TL;DR

This paper introduces a wavelet domain fusion framework that enhances detection of camouflaged moving foreground objects by highlighting subtle differences and aggregating likelihoods across wavelet bands, significantly outperforming existing methods.

Contribution

The novel fusion framework in the wavelet domain effectively improves camouflaged foreground detection by leveraging wavelet band characteristics and likelihood aggregation.

Findings

Average F-measure of 0.87 for the proposed method

Significant performance improvement over existing methods

Effective highlighting of subtle differences in wavelet bands

Abstract

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 to 0.71 to 0.8 for the other state-of-the-art methods.