scholarly journals Vision-based Hand Gesture Recognition for Mobile Service Robot Control

2018 ◽  
Author(s):  
Haile Gemgea Tamiru ◽  
Shuyan Ren ◽  
Hailong Duan
Keyword(s):  
Gesture Recognition ◽  
Robot Control ◽  
Hand Gesture Recognition ◽  
Service Robot ◽  
Hand Gesture ◽  
Mobile Service ◽  
Mobile Service Robot

Obstacle Avoidance through Gesture Recognition: Business Advancement Potential in Robot Navigation Socio-Technology

Robotica ◽  
2019 ◽  
Vol 37 (10) ◽  
pp. 1663-1676 ◽  
Author(s):  
Xuan Liu ◽  
Kashif Nazar Khan ◽  
Qamar Farooq ◽  
Yunhong Hao ◽  
Muhammad Shoaib Arshad
Keyword(s):  
Literature Review ◽  
Obstacle Avoidance ◽  
Gesture Recognition ◽  
Robot Navigation ◽  
Service Robot ◽  
Mobile Service ◽  
Modern Age ◽  
High Level ◽  
Technological Implication ◽  
Mobile Service Robot

SummaryIn the present modern age, a robot works like human and is controlled in such a manner that its movements should not create hindrance in human activities. This characteristic involves gesture feat and gesture recognition. This article is aimed to describe the developments in algorithms devised for obstacle avoidance in robot navigation which can open a new horizon for advancement in businesses. For this purpose, our study is focused on gesture recognition to mean socio-technological implication. Literature review on this issue reveals that movement of robots can be made efficient by introducing gesture-based collision avoidance techniques. Experimental results illustrated a high level of robustness and usability of the Gesture recognition (GR) system. The overall error rate is almost 10%. In our subjective judgment, we assume that GR system is very well-suited to instruct a mobile service robot to change its path on the instruction of human.

Real-Time Hand Gesture Recognition

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.

scholarly journals Integrated System for Stereoscopic Cognitive Vision, Localization, Mapping, and Communication with a Mobile Service Robot

2009 ◽  
Vol 4 (2) ◽  
pp. 118 ◽  
Author(s):  
Cătălin Buiu
Keyword(s):  
Robot Control ◽  
Integrated System ◽  
Stereoscopic Vision ◽  
Service Robot ◽  
Mobile Service ◽  
Rule Based ◽  
Communication Techniques ◽  
Waste Cleanup ◽  
Vision Localization ◽  
Mobile Service Robot

This paper describes a stereo-vision-based mobile robot that can navigate and explore its environment autonomously and safely and simultaneously building a tridimensional virtual map of the environment. The control strategy is rule-based and the interaction with robot is done via Bluetooth. The stereoscopic vision allows the robot to recognize objects and to determine the distance to the analyzed objects. The robot is able to generate and simultaneously update a full colour 3D map of the environment that is being explored. The position and type of each detected and recognized object is marked in this 3D map. Furthermore, the robot will be able to use a gripper in order to collect detected objects and carry them to dedicated collecting bins, and so will be able to work in commercial waste cleanup applications. This application represents a successful integration of computers, control and communication techniques in mobile service robot control.

scholarly journals Effective Inertial Hand Gesture Recognition Using Particle Filtering Based Trajectory Matching

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zuocai Wang ◽  
Bin Chen ◽  
Jin Wu
Keyword(s):  
Gesture Recognition ◽  
Particle Filtering ◽  
Robot Control ◽  
Inertial Sensor ◽  
Position Estimation ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method ◽  
Vision Sensors ◽  
Resampling Method

Hand gesture recognition has become more and more popular in applications like intelligent sensing, robot control, smart guidance, and so on. In this paper, an inertial sensor based hand gesture recognition method is proposed. The proposed method obtains the trajectory of the hand by using a position estimator. The proposed method utilizes the attitude estimation to produce velocity and position estimation. A particle filter (PF) is employed to estimate the attitude quaternion from gyroscope, accelerometer, and magnetometer sensors. The improvement is based on the resampling method making the original filter much faster to converge. After smoothing, the trajectory is then converted to low-definition images which are further sent to a backpropagation neural network (BP-NN) based recognizer for matching. Experiments on real-world hardware are carried out to show the effectiveness and uniqueness of the proposed method. Compared with representative methods using accelerometer or vision sensors, the proposed method is proved to be fast, reliable, and accurate.

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