The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while maintaining the object permanence even when motion is absent at some point. (ii) We collect the first large-scale Moving Camouflaged Animals (MoCA) video dataset, which consists of over 140 clips across a diverse range of animals (67 categories). (iii) We demonstrate the effectiveness of the proposed model on MoCA, and achieve competitive performance on the unsupervised segmentation protocol on DAVIS2016 by only relying on motion.

翻译：本文的目的是设计一个在视频中发现伪装的物体的计算结构,特别是利用运动信息进行物体分离,我们作出以下三项贡献:(一) 我们提议一个新结构,由两种重要组成部分组成,以打破伪装,即一个不同的注册模块,以根据背景对连续框架进行调整,有效地强调不同图像中的对象边界,一个带有内存的运动分离模块,以发现移动的物体,同时保持物体永久性,即便在某个时刻没有运动。 (二) 我们收集第一套大型移动的山旗动物(MOCA)视频数据集,由140多个短片组成,跨越各种动物(67类)。 (三) 我们只依靠运动,展示拟议的摩卡模式的有效性,并在DAVIS2016号未监督的分割协议上实现竞争性性表现。