Discovering camouflaged objects is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. While the problem of camouflaged object detection over sequential video frames has received increasing attention, the scale and diversity of existing video camouflaged object detection (VCOD) datasets are greatly limited, which hinders the deeper analysis and broader evaluation of recent deep learning-based algorithms with data-hungry training strategy. To break this bottleneck, in this paper, we construct CAMotion, a high-quality benchmark covers a wide range of species for camouflaged moving object detection in the wild. CAMotion comprises various sequences with multiple challenging attributes such as uncertain edge, occlusion, motion blur, and shape complexity, etc. The sequence annotation details and statistical distribution are presented from various perspectives, allowing CAMotion to provide in-depth analyses on the camouflaged object's motion characteristics in different challenging scenarios. Additionally, we conduct a comprehensive evaluation of existing SOTA models on CAMotion, and discuss the major challenges in VCOD task. The benchmark is available at https://www.camotion.focuslab.net.cn, we hope that our CAMotion can lead to further advancements in the research community.

翻译：发现伪装物体是计算机视觉中的一项挑战性任务，因为伪装物体与其周围环境具有高度相似性。尽管基于连续视频帧的伪装物体检测问题日益受到关注，但现有视频伪装物体检测（VCOD）数据集的规模和多样性严重受限，这阻碍了对近期依赖数据饥饿训练策略的深度学习算法的更深入分析和更广泛评估。为突破这一瓶颈，本文构建了CAMotion——一个覆盖多种物种的野外伪装运动目标检测高质量基准。CAMotion包含具有不确定边缘、遮挡、运动模糊、形状复杂度等多重挑战属性的各类序列。我们从多个角度呈现了序列标注细节和统计分布，使CAMotion能够深入分析不同挑战场景下伪装物体的运动特性。此外，我们对现有SOTA模型在CAMotion上进行了全面评估，并讨论了VCOD任务中的主要挑战。该基准数据集可通过https://www.camotion.focuslab.net.cn获取，我们期望CAMotion能推动研究社区的进一步进展。