Foreground Detection in Camouflaged Scenes

TL;DR

This paper introduces a texture-guided weighted voting method utilizing wavelet transforms to improve foreground detection in camouflaged scenes where traditional methods struggle due to similar appearance of foreground and background.

Contribution

The paper presents a novel wavelet-based approach with a weighted voting scheme that effectively detects camouflaged foreground objects, outperforming existing methods.

Findings

Achieves superior detection accuracy in camouflaged scenes

Effectively captures subtle differences using wavelet frequency bands

Outperforms current state-of-the-art methods

Abstract

Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show visually distinct characteristics and thus the foreground can be detected once a good background model is obtained. However, there are many situations where this is not the case. Of particular interest in video surveillance is the camouflage case. For example, an active attacker camouflages by intentionally wearing clothes that are visually similar to the background. In such cases, even given a decent background model, it is not trivial to detect foreground objects. This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes. The proposed method employs the stationary wavelet transform to decompose the image into frequency bands. We show that the small and hardly noticeable differences between foreground and background in the image domain can be effectively captured in certain wavelet frequency bands. To make the final foreground decision, a weighted voting scheme is developed based on intensity and texture of all the wavelet bands with weights carefully designed. Experimental results demonstrate that the proposed method achieves superior performance compared to the current state-of-the-art results.