Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
Hala Lamdouar,Charig Yang,Weidi Xie,Andrew Zisserman +3 more
- pp 488-503
TLDR
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, 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.Abstract:
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.read more
Citations
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Proceedings ArticleDOI
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way
TL;DR: This work designs a new discriminative mask which makes the model attend on the fixation and edge regions, and proposes an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion.
Proceedings ArticleDOI
Implicit Motion Handling for Video Camouflaged Object Detection
Xuelian Cheng,Huan Xiong,Deng-Ping Fan,Yiran Zhong,Mehrtash Harandi,Tom Drummond,Zongyuan Ge +6 more
TL;DR: A new video camouflaged object detection framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames is proposed and unifies motion estimation and object segmentation within a single optimization framework.
Journal ArticleDOI
Camouflaged Object Detection via Context-Aware Cross-Level Fusion
TL;DR: A novel Context-aware Cross-level Fusion Network, which fuses context-aware cross-level features for accurately identifying camouflaged objects, and is an effective COD model and outperforms SOTA models remarkably.
Journal ArticleDOI
Feature Aggregation and Propagation Network for Camouflaged Object Detection
TL;DR: Wang et al. as discussed by the authors proposed a novel Feature Aggregation and Propagation Network (FAP-Net) to capture the scale variations of the camouflaged objects and formulated a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information.
Proceedings ArticleDOI
Modeling Aleatoric Uncertainty for Camouflaged Object Detection
TL;DR: This paper presents a confidence-aware camouflaged object detection (COD) framework using dynamic supervision to produce both an accurate camouflage map and a reliable "aleatoric uncertainty", and claims that, once trained, the confidence estimation network can evaluate the pixel-wise accuracy of the prediction without relying on the ground truth camouflage map.
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