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

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.

arXiv comment13 pages, accepted by IEEE TIP

References in corpus (3)

• Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
• Background Modeling Using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space
• On the Role and the Importance of Features for Background Modeling and Foreground Detection

Cited by in corpus (4)

• Feature Aggregation and Propagation Network for Camouflaged Object Detection
• Camouflaged Object Detection and Tracking: A Survey
• Utilizing Grounded SAM for self-supervised frugal camouflaged human detection
• CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection