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

arXiv: https://arxiv.org/abs/1804.05984 · HTML: https://mlsanity.com/paper/1804.05984/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [Feature Aggregation and Propagation Network for Camouflaged Object Detection](https://mlsanity.com/paper/2212.00990/) (2022)
- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer](https://mlsanity.com/paper/2205.10579/) (2022)
- [Exploring Depth Contribution for Camouflaged Object Detection](https://mlsanity.com/paper/2106.13217/) (2021)
- [Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection](https://mlsanity.com/paper/2403.01968/) (2024)
- [Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark](https://mlsanity.com/paper/2310.04253/) (2023)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [Utilizing Grounded SAM for self-supervised frugal camouflaged human detection](https://mlsanity.com/paper/2406.05776/) (2024)
- [CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection](https://mlsanity.com/paper/2607.01870/) (2026)
- [Event Detection in Videos: A Framework for the Development of New Methods](https://mlsanity.com/paper/2607.04372/) (2026)

## Abstract (arXiv)

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.