Rethinking Camouflaged Object Detection: Models and Datasets

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 9; pp. 5708 - 5724
• Main Authors: Bi, Hongbo, Zhang, Cong, Wang, Kang, Tong, Jinghui, Zheng, Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: benchmark; Camouflaged object detection; Computer vision; Datasets; Deep learning; Feature extraction; handcrafted feature-based; Image color analysis; Image segmentation; Motion perception; Object detection; Object recognition; Optical flow (image analysis); Optical imaging; Performance evaluation; Task analysis; Visual tasks; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2021.3124952

Camouflaged object detection (COD) is an emerging visual detection task, which aims to locate and distinguish the disguised target in complex backgrounds by imitating the human visual detection system. Recently, COD has attracted increasing attention in computer vision, and a few models of camouflaged object detection have been successfully explored. However, most existing works primarily focus on modeling camouflaged object detection over in-depth analyzing existing COD structures. To the best of our knowledge, a systematic review for COD has not been publicly reported, especially for recently proposed deep learning-based COD models. To make up this vacancy, we firstly proposed a comprehensive review on both COD models and public benchmark datasets and provide potential directions for future COD studies. Specifically, we conduct a comprehensive summary of 39 existing COD models from 1998 to 2021. And then, to facilitate subsequent research on COD, we classify the existing structures into two categories, 27 traditional handcrafted feature-based structures and 12 structures based on deep learning. In addition, we further group traditional handcrafted feature-based structures into six sub-classes based on the detection mechanism: texture, color, motion, intensity, optical flow, and multi-modal fusion. Furthermore, we take an in-depth analysis of the deep learning-based structure based on both detection motivation and detection performance and evaluate the performance of each structure. Moreover, we sum up four widely used COD datasets and describe the details of each one. Finally, we also discuss the limitations of COD and the corresponding solutions to improve detection accuracy. We still mention the relevant applications of camouflaged object detection and its future research directions to promote the development of camouflaged object detection.