The Making and Breaking of Camouflage
Hala Lamdouar Weidi Xie Andrew Zisserman
Not all camouflages are equally effective, as even a partially visible
contour or a slight color difference can make the animal stand out and break
its camouflage. In this paper, we address the question of what makes a
camouflage successful, by proposing three scores for automatically assessing
its effectiveness. In particular, we show that camouflage can be measured by
the similarity between background and foreground features and boundary
visibility. We use these camouflage scores to assess and compare all available
camouflage datasets. We also incorporate the proposed camouflage score into a
generative model as an auxiliary loss and show that effective camouflage images
or videos can be synthesised in a scalable manner. The generated synthetic
dataset is used to train a transformer-based model for segmenting camouflaged
animals in videos. Experimentally, we demonstrate state-of-the-art camouflage
breaking performance on the public MoCA-Mask benchmark.