Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

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.

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Real-Time Hand Gesture Recognition Using the Leap Motion Controller and Machine Learning

2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI) ◽

10.1109/la-cci47412.2019.9037037 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ruben Nogales ◽

Marco Benalcazar

Keyword(s):

Machine Learning ◽

Real Time ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Leap Motion ◽

Motion Controller ◽

Leap Motion Controller

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Real Time Hand Gesture Recognition Using Leap Motion Controller Based on CNN-SVM Architechture

2021 IEEE 7th International Conference on Virtual Reality (ICVR) ◽

10.1109/icvr51878.2021.9483844 ◽

2021 ◽

Author(s):

Aamrah Ikram ◽

Yue Liu

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Leap Motion ◽

Motion Controller ◽

Leap Motion Controller

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Real-Time 3D Hand Gesture Recognition from Depth Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2826 ◽

2013 ◽

Vol 765-767 ◽

pp. 2826-2829 ◽

Cited By ~ 1

Author(s):

Song Lin ◽

Rui Min Hu ◽

Yu Lian Xiao ◽

Li Yu Gong

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Point Cloud ◽

Hand Gesture Recognition ◽

Microsoft Kinect ◽

Depth Image ◽

Support Vector ◽

Hand Gesture ◽

Depth Information ◽

Moment Invariant

In this paper, we propose a novel real-time 3D hand gesture recognition algorithm based on depth information. We segment out the hand region from depth image and convert it to a point cloud. Then, 3D moment invariant features are computed at the point cloud. Finally, support vector machine (SVM) is employed to classify the shape of hand into different categories. We collect a benchmark dataset using Microsoft Kinect for Xbox and test the propose algorithm on it. Experimental results prove the robustness of our proposed algorithm.

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Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network

Sensors ◽

10.3390/s20072106 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2106 ◽

Cited By ~ 2

Author(s):

Linchu Yang ◽

Ji’an Chen ◽

Weihang Zhu

Keyword(s):

Neural Network ◽

Gesture Recognition ◽

Recurrent Neural Network ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Leap Motion ◽

Motion Controller ◽

Hand Gestures ◽

Leap Motion Controller ◽

Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition is one of the most significant tools for human–computer interaction. In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the recognition of dynamic hand gestures from a Leap Motion Controller (LMC) is proposed. In addition, based on LMC, an efficient way to capture the dynamic hand gestures is identified. Dynamic hand gestures are represented by sets of feature vectors from the LMC. The proposed system has been tested on the American Sign Language (ASL) datasets with 360 samples and 480 samples, and the Handicraft-Gesture dataset, respectively. On the ASL dataset with 360 samples, the system achieves accuracies of 100% and 96.3% on the training and testing sets. On the ASL dataset with 480 samples, the system achieves accuracies of 100% and 95.2%. On the Handicraft-Gesture dataset, the system achieves accuracies of 100% and 96.7%. In addition, 5-fold, 10-fold, and Leave-One-Out cross-validation are performed on these datasets. The accuracies are 93.33%, 94.1%, and 98.33% (360 samples), 93.75%, 93.5%, and 98.13% (480 samples), and 88.66%, 90%, and 92% on ASL and Handicraft-Gesture datasets, respectively. The developed system demonstrates similar or better performance compared to other approaches in the literature.

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Real-Time Hand Gesture Recognition

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2173132 ◽

2021 ◽

pp. 618-624

Author(s):

Pranjali Manmode ◽

Rupali Saha ◽

Manisha N. Amnerkar

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Skin Color ◽

Robot Control ◽

Rapid Development ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Human Hand ◽

Hand Gestures ◽

Gesture Tracking

With the rapid development of computer vision, the demand for interaction between humans and machines is becoming more and more extensive. Since hand gestures can express enriched information, hand gesture recognition is widely used in robot control, intelligent furniture, and other aspects. The paper realizes the segmentation of hand gestures by establishing the skin color model and AdaBoost classifier based on haar according to the particularity of skin color for hand gestures and the denaturation of hand gestures with one frame of video being cut for analysis. In this regard, the human hand is segmented from a complicated background. The camshaft algorithm also realizes real-time hand gesture tracking. Then, the area of hand gestures detected in real-time is recognized by a convolutional neural network to discover the recognition of 10 common digits. Experiments show 98.3% accuracy.

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Real-Time Hand Gesture Recognition Using KNN-DTW and Leap Motion Controller

Information and Communication Technologies - Communications in Computer and Information Science ◽

10.1007/978-3-030-62833-8_8 ◽

2020 ◽

pp. 91-103

Author(s):

Rubén Nogales ◽

Marco Benalcázar

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Leap Motion ◽

Motion Controller ◽

Leap Motion Controller

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Real-Time Hand Gesture Recognition Using Finger Segmentation

The Scientific World JOURNAL ◽

10.1155/2014/267872 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 22

Author(s):

Zhi-hua Chen ◽

Jung-Tae Kim ◽

Jianning Liang ◽

Jing Zhang ◽

Yu-Bo Yuan

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Background Subtraction ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Subtraction Method ◽

Hand Gestures ◽

Data Set ◽

Hand Region ◽

State Of Art

Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures.

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