scholarly journals DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework

2020 ◽  
Author(s):  
Okan Kopuklu ◽  
Thomas Ledwon ◽  
Yao Rong ◽  
Neslihan Kose ◽  
Gerhard Rigoll
Keyword(s):  
Real Time ◽  
Hand Gestures ◽  
Dynamic Recognition

Real-time Robotic Arm Control Embedded System using Hand Gestures

2021 ◽  
Vol 19 (11) ◽  
pp. 45-53
Author(s):  
Chung-Geun Kim ◽  
Eun-Su Kim ◽  
Jae-Wook Shin ◽  
Bum-Yong Park
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Robotic Arm ◽  
Hand Gestures ◽  
Robotic Arm Control

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

Sign in / Sign up

Export Citation Format

Share Document