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A fiducial marker system usually consists of markers, a detection algorithm,and a coding system. The appearance of markers and the detection robustness aregenerally limited by the existing detection algorithms, which are hand-craftedwith traditional low-level image processing techniques. Furthermore, asophisticatedly designed coding system is required to overcome the shortcomingsof both markers and detection algorithms. To improve the flexibility androbustness in various applications, we propose a general deep learning basedframework, DeepTag, for fiducial marker design and detection. DeepTag not onlysupports detection of a wide variety of existing marker families, but alsomakes it possible to design new marker families with customized local patterns.Moreover, we propose an effective procedure to synthesize training data on thefly without manual annotations. Thus, DeepTag can easily adapt to existing andnewly-designed marker families. To validate DeepTag and existing methods,beside existing datasets, we further collect a new large and challengingdataset where markers are placed in different view distances and angles.Experiments show that DeepTag well supports different marker families andgreatly outperforms the existing methods in terms of both detection robustnessand pose accuracy. Both code and dataset are available athttps://herohuyongtao.github.io/research/publications/deep-tag/.

DeepTag: A General Framework for Fiducial Marker Design and Detection