scholarly journals TWO-STREAM CONVOLUTIONAL NETWORK FOR DYNAMIC HAND GESTURE RECOGNITION USING CONVOLUTIONAL LONG SHORT-TERM MEMORY NETWORKS

2020 ◽  
Vol 58 (4) ◽  
pp. 514
Author(s):  
Phat Huu Nguyen ◽  
Tien Ngoc Luong
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Recognition System ◽  
Human Action ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Convolutional Network ◽  
Healthcare Facilities ◽  
Execution Speed ◽  
Dynamic Hand Gesture Recognition

Human action and gesture recognition provides important and worth information for interaction between human and device ambient that monitors living, healthcare facilities or entertainment activities in smart homes. Recent years, there were many machine learning model application studies to recognize human action and gesture. In this paper, we propose a dynamic hand gesture recognition system in video based on two stream-convolution network (ConvNet) architecture. Specifically, we research the state-of-the-art approaches using to recognize dynamic hand gesture in video and propose an improvement method to enhance performance of model which is suitable for uses such as indoor environment in this paper. Our contribution is improvement of two stream ConvNet to achieve better performance. The results show that the proposal model improves execution speed and memory resource usage comparing to existing models.

HUMAN-COMPUTER INTERACTION USING DYNAMIC HAND GESTURE RECOGNITION TO CONVENIENTLY CONTROL THE SYSTEM

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Smit Parikh ◽  
Srikar Banka ◽  
Isha Lautrey ◽  
Isha Gupta ◽  
Prof Dhanalekshmi Yedurkar
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Basic Functions ◽  
Dynamic Hand Gesture Recognition ◽  
Basic Features ◽  
Computer Interaction

The use of a physical controller such as a mouse, a keyboard for human computer interaction hinders the natural interface since the user and computer have a high barrier. Our aim is to create an application that controls some basic features of computers using hand gestures through an integrated webcam to resolve this issue. A Hand Gesture Recognition system detects gestures and translates them into specific actions to make our work easier. This can be pursued using OpenCV to capture the gestures which will be interfaced using Django, React.Js and Electron. An algorithm named YOLO is used to train the system accordingly. The gestures will get saved inside the DBMS. The main result expected is that the user will be able to control the basic functions of the system using his/her hand gestures and hence providing them utmost comfort.

scholarly journals Spatial temporal graph convolutional networks for skeleton-based dynamic hand gesture recognition

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yong Li ◽  
Zihang He ◽  
Xiang Ye ◽  
Zuguo He ◽  
Kangrong Han
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Feature Representation ◽  
Training Dataset ◽  
Hand Gesture ◽  
Semantic Features ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Relative Coordinates ◽  
Dynamic Hand Gesture Recognition

Abstract Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. In literature, several methods have been proposed to recognize hand gestures with skeletons. One problem with these methods is that they consider little the connectivity between the joints of a skeleton, constructing simple graphs for skeleton connectivity. Observing this, we built a new model of hand skeletons by adding three types of edges in the graph to finely describe the linkage action of joints. Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn more semantic features. Furthermore, relative coordinates are employed to help hand gesture graph convolutional network learn the feature representation independent of the random starting positions of actions. The proposed method is validated on two challenging datasets, and the experimental results show that it outperforms the state-of-the-art methods. Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition.

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