DeepTag: A General Framework for Fiducial Marker Design and Detection

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 3; pp. 2931 - 2944
• Main Authors: Zhang, Zhuming, Hu, Yongtao, Yu, Guoxing, Dai, Jingwen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Coding; Convolutional neural networks; Datasets; deep learning; Design; Detection algorithms; Fiducial marker; Image coding; Image processing; Machine learning; marker design; monocular pose estimation; object detection; Pose estimation; Robustness; Symbols; Training data
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2022.3174603

A fiducial marker system usually consists of markers, a detection algorithm, and a coding system. The appearance of markers and the detection robustness are generally limited by the existing detection algorithms, which are hand-crafted with traditional low-level image processing techniques. Furthermore, a sophisticatedly designed coding system is required to overcome the shortcomings of both markers and detection algorithms. To improve the flexibility and robustness in various applications, we propose a general deep learning based framework, DeepTag , for fiducial marker design and detection. DeepTag not only supports detection of a wide variety of existing marker families, but also makes it possible to design new marker families with customized local patterns. Moreover, we propose an effective procedure to synthesize training data on the fly without manual annotations. Thus, DeepTag can easily adapt to existing and newly-designed marker families. To validate DeepTag and existing methods, beside existing datasets, we further collect a new large and challenging dataset where markers are placed in different view distances and angles. Experiments show that DeepTag well supports different marker families and greatly outperforms the existing methods in terms of both detection robustness and pose accuracy. Both code and dataset are available at https://herohuyongtao.github.io/research/publications/deep-tag/ .