Dataset & Benchmark

CAMotion

CAmouflaged Moving object detection

Paper Download Evaluation
GitHub

Introduction

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, we construct CAMotion, a high-quality benchmark that 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.

CAMotion benchmark teaser figure
Figure 1. Examples from the CAMotion dataset with corresponding pixel-level annotations. Rows 1, 3, 5, and 7 show original images, while Rows 2, 4, 6, and 8 present the corresponding pixel-wise ground-truth annotations.

Statistics

149,319 Image Frames
6.51×MoCA-Mask
30,028 Annotated Frames
6.40×MoCA-Mask
152 Species
3.45×MoCA-Mask
Statistic comparison with other camouflage datasets
TABLE 1: Statistics of camouflage datasets. * indicates that the #Species is not reported in the original paper and is estimated by us.
Statistical comparison table of camouflage datasets
Figure 2: The scale and species comparison between existing camouflage datasets and CAMotion.
Figure 2: scale and species comparison between camouflage datasets and CAMotion
Figure 3: Scale distribution comparison of CAMotion and MoCA-Mask. Note that the reported ratio is defined as the proportion of foreground area relative to the entire image.
Figure 3: scale distribution comparison of CAMotion and MoCA-Mask

Dataset features

Figure 4 dataset feature visualization
Figure 4: Taxonomic structure of CAMotion. The inner ring illustrates the class taxonomy, and the outer ring shows the corresponding order taxonomy.
Figure 5 dataset feature visualization
Figure 5: An example of the hierarchy tree in CAMotion, illustrated with the Ray-finned Fish class.
Figure 6 dataset feature visualization
Figure 6: The attributes distribution of CAMotion in frame-level and sequence-level.

Tip: click to enlarge for details.

Figure 7 statistics for CAMotion dataset
Figure 7: Statistics for CAMotion dataset. (a) Object sizes distribution. (b) The distribution of video durations. (c) Global and local contrast distribution. (d) Motion statistics of the camouflaged objects. Note that the reported ratio is defined as the proportion of foreground area relative to the entire image.

Demo

Amazon Leaffish
Batfish
Cat
Clownfish
Common Octopus
Eurasian Bittern
Leaf-Tailed Geckos
Leafy Seadragon
Mockingbird
Moss Mimic Stick Insect
Orchid Mantis
Peppered Moth
Pygmy Seahorse
Snow Leopard
Snowy Owl
Stoat

Tasks & Evaluation

We report results under a unified protocol: define input (e.g., first-frame mask or box), allowed pretraining, and evaluation metrics. [TBD：1–2 句概括主实验设定，可引用论文 Sec.]

  • Metrics: mIoU, F-measure / S-measure, MAE [TBD：与终稿一致]
  • Protocol: [TBD：是否允许光流/外训数据等]
  • Test submission: [TBD：评测服务器提交流程一句话]

Table 2. Main benchmark results on CAMotion (placeholder). [TBD：从论文主表复制；可加「相对基线降幅」等一句 MOSE 式总结]

Method Backbone Setting mIoU MAE
Baseline-AREPLACESup.REPLACEREPLACE
Baseline-BREPLACESup.REPLACEREPLACE
OursREPLACESup.REPLACEREPLACE

Full leaderboard on Evaluation Server

Dataset

Dataset Download

The dataset is available for non-commercial research purposes only. Please use the following links.

Google Drive Baidu Netdisk

Attributes

To facilitate in-depth analysis of camouflaged videos under various challenging scenarios, we categorize each camouflaged frame according to eight attributes, including uncertain edge (UE), big object (BO), multiple objects (MO), small object (SO), occlusion (OC), shape complexity (SC), out-of-view (OV) and motion blur (MB). The definitions of these attributes are provided below.

Attr Description
MO Multiple Objects: image contains at least two objects.
BO Big Object: ratio between object area and image area ≥ 0.15.
SO Small Object: ratio between object area and image area ≤ 0.02.
UE Uncertain Edge: the foreground and background areas around object have similar colors and textures.
OC Occlusion: the object is partially occluded.
SC Shape Complexity: object contains thin parts (e.g., animal foot).
OV Out-of-View: some portion of the object leaves the camera field of view.
MB Motion Blur: the object region is blurred due to the motion of object or camera.

Data structure

train_valid.tar.gz
│
├── Annotations
│   ├── video_name_1
│   │   ├── 00000.png
│   │   └── ...
│   └── ...
│
└── JPEGImages
    ├── video_name_1
    │   ├── 00000.jpg
    │   └── ...
    └── ...

People

Siyuan Yao

Sun Yat-sen University Shenzhen Campus

Hao Sun

Sun Yat-sen University Shenzhen Campus

Ruiqi Yu

Nanyang Technological University

Wenqi Ren

Sun Yat-sen University Shenzhen Campus

Xiaochun Cao

Sun Yat-sen University Shenzhen Campus

Citation

Please cite CAMotion if it helps your research.

@article{camotion,
  title   = {CAMotion: A High-Quality Dataset for Camouflaged Motion Object Detection in the Wild},
  author  = {Siyuan Yao and Hao Sun and Hai Long and Ruiqi Yu and Jiehong Li and Xiwei Jiang and Yanzhao Su and Wenqi Ren and Xiaochun Cao},
  journal = {Under review},
  year    = {2026}
}