构建首个野外伪装运动目标检测高质量基准数据集
CAMotion: A High-Quality Benchmark for Camouflaged Moving Object Detection in the Wild
- 构建涵盖多种生物的野外视频数据集，包含复杂运动与遮挡场景
- 提供多维度标注与统计分析，揭示伪装目标运动特性
- 支持主流模型评估，推动伪装目标检测算法研究
由于伪装目标与其周围环境高度相似，发现伪装物体是计算机视觉中的难题。尽管序列视频中伪装目标检测问题日益受到关注，但现有视频伪装目标检测（VCOD）数据集规模和多样性严重受限，制约了基于深度学习算法的深入分析与广泛评估。为此，本文构建了CAMotion——一个覆盖多种物种、面向野外场景的高质量伪装运动目标检测基准数据集。该数据集包含多种具有不确定边缘、遮挡、运动模糊和形状复杂性等挑战属性的视频序列，提供了从多个角度出发的序列标注细节与统计分布，支持对不同挑战场景下伪装目标运动特性的深入分析。此外，我们对现有最先进模型在该数据集上进行了全面评估，并讨论了VCOD任务的主要挑战。数据集已开放访问：https://www.camotion.focuslab.net.cn，期待其推动社区研究进展。
原文摘要 · Abstract (English)
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.
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