A System to Interact with CAVE Applications Using Hand Gesture Recognition from Depth Data

2014 ◽  
Author(s):  
Diego Augusto T.Q. Leite ◽  
Julio Cesar Duarte ◽  
Jauvane C. Oliveira ◽  
Victor De Almeida Thomaz ◽  
Gilson A. Giraldi
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Depth Data

scholarly journals DBSCAN for Hand Tracking and Gesture Recognition

2020 ◽  
Vol 5 (2) ◽  
pp. 168
Author(s):  
Wisnu Aditya ◽  
Herman Tolle ◽  
Timothy K Shih
Keyword(s):  
Gesture Recognition ◽  
Clustering Algorithm ◽  
Hand Gesture Recognition ◽  
Computational Time ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Depth Data ◽  
Dbscan Clustering ◽  
Hand Segmentation ◽  
Speed Up

Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

Contour Based Hand Gesture Recognition Using Depth Data

2013 ◽  
Author(s):  
Le Tran Nguyen ◽  
◽  
Cong Do Thanh ◽  
Tung Nguyen Ba ◽  
Cuong Ta Viet ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Depth Data

scholarly journals A Combined Method of Skin-and Depth-based Hand Gesture Recognition

2019 ◽  
Vol 17 (1) ◽  
pp. 137-145
Author(s):  
Tukhtaev Sokhib ◽  
Taeg Keun Whangbo
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Hand Gesture ◽  
Combined Method ◽  
Depth Data ◽  
Acquisition Device ◽  
Hand Region

Kinect is a promising acquisition device that provides useful information on a scene through color and depth data. There has been a keen interest in utilizing Kinect in many computer vision areas such as gesture recognition. Given the advantages that Kinect provides, hand gesture recognition can be deployed efficiently with minor drawbacks. This paper proposes a simple and yet efficient way of hand gesture recognition via segmenting a hand region from both color and depth data acquired by Kinect v1. The Inception model of the image recognition system is used to check the reliability of the proposed method. Experimental results are derived from a sample dataset of Microsoft Kinect hand acquisitions. Under the appropriate conditions, it is possible to achieve high accuracy in close to real time

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