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Journal Article
Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect
- Authors
- Chanhwi Lee, Jaehan Kim, Seoungbae Cho, Jinwoong Kim, Jisang Yoo, Soonchul Kwon
- Issue Date
- 2020-08
- Citation
- Sensors, v.20, no.16, pp.1-13
- ISSN
- 1424-8220
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.
- KSP Keywords
- Depth camera, Depth image, Hand detection, Hand gesture recognition system, Leap Motion, Learning model, continuous rotation, deep learning(DL), detection performance, gesture interaction, holographic display
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
