Journal
PATTERN RECOGNITION
Volume 93, Issue -, Pages 251-272Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2019.04.026
Keywords
Hand pose estimation; Hand tracking; Depth camera; Human-computer interaction
Funding
- National Natural Science Foundation of China [51821093, U1608256]
- Zhejiang Provincial Key Development Program of China [2017C01045]
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3D Hand pose estimation has received an increasing amount of attention, especially since consumer depth cameras came onto the market in 2010. Although substantial progress has occurred recently, no overview has kept up with the latest developments. To bridge the gap, we provide a comprehensive survey, including depth cameras, hand pose estimation methods, and public benchmark datasets. First, a markerless approach is proposed to evaluate the tracking accuracy of depth cameras with the aid of a numerical control linear motion guide. Traditional approaches focus only on static characteristics. The evaluation of dynamic tracking capability has been long neglected. Second, we summarize the state-of-the-art methods and analyze the lines of research. Third, existing benchmark datasets and evaluation criteria are identified to provide further insight into the field of hand pose estimation. In addition, realistic challenges, recent trends, dataset creation and annotation, and open problems for future research directions are also discussed. (C) 2019 Elsevier Ltd. All rights reserved.
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