A survey on 3D hand pose estimation: Cameras, methods, and datasets. (September 2019)

• Record Type: Journal Article
• Authors: Li, Rui; Liu, Zhenyu; Tan, Jianrong
• Is Part Of: Pattern recognition. Volume 93(2019:Sep.)
• Page Start: 251
• Page End: 272
• Publication Date: 2019-09
• Subjects: Hand pose estimation -- Hand tracking -- Depth camera -- Human-computer interaction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.04.026
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A markerless approach is proposed to evaluate the tracking accuracy of a depth camera with a numerical control linear motion guide. State-of-the-art hand pose estimation methods are intuitively summarized with tables, and their research lines are detailly analyzed. Public datasets and evaluation criteria are identified to provide further insight into the field of hand pose estimation. Realistic challenges, recent trends, dataset creation and annotation, and open problems for future research directions are outlined. Abstract: 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.