A Gesture Recognition Method Based on YCbCr and SURF for Service Robot Interaction

2021 ◽  
Author(s):  
Jia Zhang ◽  
Tao Geng ◽  
Hu Shi ◽  
Danyang Wang ◽  
Jiangtao Lu
Keyword(s):  
Gesture Recognition ◽  
Service Robot ◽  
Recognition Method ◽  
Robot Interaction

scholarly journals Online control programming algorithm for human–robot interaction system with a novel real-time human gesture recognition method

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986176 ◽  
Author(s):  
Bo Chen ◽  
Chunsheng Hua ◽  
Bo Dai ◽  
Yuqing He ◽  
Jianda Han
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Robot Control ◽  
Recognition System ◽  
Online Control ◽  
Human Robot Interaction ◽  
Programming Algorithm ◽  
Recognition Method ◽  
Robot Interaction ◽  
Real Time Processing

This article proposes an online control programming algorithm for human–robot interaction systems, where robot actions are controlled by the recognition results of gestures performed by human operators based on visual images. In contrast to traditional robot control systems that use pre-defined programs to control a robot where the robot cannot change its tasks freely, this system allows the operator to train online and replan human–robot interaction tasks in real time. The proposed system is comprised of three components: an online personal feature pretraining system, a gesture recognition system, and a task replanning system for robot control. First, we collected and analyzed features extracted from images of human gestures and used those features to train the recognition program in real time. Second, a multifeature cascade classifier algorithm was applied to guarantee both the accuracy and real-time processing of our gesture recognition method. Finally, to confirm the effectiveness of our algorithm, we selected a flight robot as our test platform to conduct an online robot control experiment based on the visual gesture recognition algorithm. Through extensive experiments, the effectiveness and efficiency of our method has been confirmed.

scholarly journals Human–Robot Interaction by Understanding Upper Body Gestures

2014 ◽  
Vol 23 (2) ◽  
pp. 133-154 ◽  
Author(s):  
Yang Xiao ◽  
Zhijun Zhang ◽  
Aryel Beck ◽  
Junsong Yuan ◽  
Daniel Thalmann
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Human Robot Interaction ◽  
Upper Body ◽  
Hand Posture ◽  
Motion Generation ◽  
Recognition Method ◽  
Robot Interaction ◽  
Data Set ◽  
Verbal Language

In this paper, a human–robot interaction system based on a novel combination of sensors is proposed. It allows one person to interact with a humanoid social robot using natural body language. The robot understands the meaning of human upper body gestures and expresses itself by using a combination of body movements, facial expressions, and verbal language. A set of 12 upper body gestures is involved for communication. This set also includes gestures with human–object interactions. The gestures are characterized by head, arm, and hand posture information. The wearable Immersion CyberGlove II is employed to capture the hand posture. This information is combined with the head and arm posture captured from Microsoft Kinect. This is a new sensor solution for human-gesture capture. Based on the posture data from the CyberGlove II and Kinect, an effective and real-time human gesture recognition method is proposed. The gesture understanding approach based on an innovative combination of sensors is the main contribution of this paper. To verify the effectiveness of the proposed gesture recognition method, a human body gesture data set is built. The experimental results demonstrate that our approach can recognize the upper body gestures with high accuracy in real time. In addition, for robot motion generation and control, a novel online motion planning method is proposed. In order to generate appropriate dynamic motion, a quadratic programming (QP)-based dual-arms kinematic motion generation scheme is proposed, and a simplified recurrent neural network is employed to solve the QP problem. The integration of a handshake within the HRI system illustrates the effectiveness of the proposed online generation method.

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

2020 ◽  
Vol 29 (6) ◽  
pp. 1153-1164
Author(s):  
Qianyi Xu ◽  
Guihe Qin ◽  
Minghui Sun ◽  
Jie Yan ◽  
Huiming Jiang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Feature Fusion ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method
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