Problem
Research questions and friction points this paper is trying to address.
camouflaged object detection
video camouflaged object detection
benchmark dataset
motion characteristics
challenging scenarios
CAMotion: A High-Quality Benchmark for Camouflaged Moving Object Detection in the Wild
🤖 AI Summary
Existing video-based camouflaged object detection datasets suffer from limited scale and insufficient diversity, hindering in-depth exploration of deep learning models. To address this gap, this work presents CAMotion, the first large-scale benchmark for detecting camouflaged moving objects in unconstrained natural scenes. CAMotion encompasses multiple species, complex dynamic backgrounds, and diverse challenges—including occlusion, motion blur, and ambiguous boundaries—and features high-quality manual annotations augmented with multi-dimensional attribute labels, along with comprehensive sequence-level annotations and statistical analyses. Systematic evaluation of state-of-the-art models on CAMotion reveals critical limitations in current approaches, particularly in temporal modeling and the integration of appearance and motion cues, thereby establishing a solid foundation and clear direction for future research.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Camouflaged Moving Object Detection
Video Camouflaged Object Detection (VCOD)
Benchmark Dataset
Motion Characteristics
Challenging Scenarios
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