A 28 nm, 397 μW real-time dynamic gesture recognition chip based on RISC-V processor

2021 ◽  
pp. 105219
Author(s):  
Yong-Liang Zhang ◽  
Qiang Li ◽  
Hui Zhang ◽  
Wei-Zhen Wang ◽  
Jun Han ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Time Dynamic ◽  
Dynamic Gesture Recognition ◽  
28 Nm

Real-time dynamic gesture recognition and hand servo tracking using PTZ camera

2019 ◽  
Vol 78 (19) ◽  
pp. 27403-27424 ◽  
Author(s):  
Songxiao Cao ◽  
Xuanyin Wang
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Time Dynamic ◽  
Ptz Camera ◽  
Servo Tracking ◽  
Dynamic Gesture Recognition

A Real-Time Dynamic Gesture Recognition System

2013 ◽  
Vol 333-335 ◽  
pp. 849-855 ◽  
Author(s):  
Jiang Guo ◽  
Jun Cheng ◽  
Yu Guo ◽  
Jian Xin Pang
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Sensory Information ◽  
Recognition System ◽  
Correction Method ◽  
Hand Detection ◽  
Time Dynamic ◽  
Trajectory Correction ◽  
Dynamic Time ◽  
Dynamic Gesture Recognition

In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dynamic time warping (DTW) algorithm is used to perform the time alignment and normalization by computing a temporal transformation allowing the two signals to be matched. Experiment results show our proposed gesture recognition system achieve well result in real time.

scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

Design of a Real-Time Human-Robot Collaboration System Using Dynamic Gestures

2020 ◽  
Author(s):  
Haodong Chen ◽  
Ming C. Leu ◽  
Wenjin Tao ◽  
Zhaozheng Yin
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Industrial Robot ◽  
Classification Model ◽  
Robotic Arm ◽  
The Real ◽  
Time Motion ◽  
Dynamic Gestures ◽  
Dynamic Gesture Recognition ◽  
Human Robot Collaboration

Abstract With the development of industrial automation and artificial intelligence, robotic systems are developing into an essential part of factory production, and the human-robot collaboration (HRC) becomes a new trend in the industrial field. In our previous work, ten dynamic gestures have been designed for communication between a human worker and a robot in manufacturing scenarios, and a dynamic gesture recognition model based on Convolutional Neural Networks (CNN) has been developed. Based on the model, this study aims to design and develop a new real-time HRC system based on multi-threading method and the CNN. This system enables the real-time interaction between a human worker and a robotic arm based on dynamic gestures. Firstly, a multi-threading architecture is constructed for high-speed operation and fast response while schedule more than one task at the same time. Next, A real-time dynamic gesture recognition algorithm is developed, where a human worker’s behavior and motion are continuously monitored and captured, and motion history images (MHIs) are generated in real-time. The generation of the MHIs and their identification using the classification model are synchronously accomplished. If a designated dynamic gesture is detected, it is immediately transmitted to the robotic arm to conduct a real-time response. A Graphic User Interface (GUI) for the integration of the proposed HRC system is developed for the visualization of the real-time motion history and classification results of the gesture identification. A series of actual collaboration experiments are carried out between a human worker and a six-degree-of-freedom (6 DOF) Comau industrial robot, and the experimental results show the feasibility and robustness of the proposed system.

Real-Time Static and Dynamic Gesture Recognition Using Mixed Space Features for 3D Virtual World's Interactions

2018 ◽  
Author(s):  
S. P. Kasthuri Arachchi ◽  
Noorkholis Luthfil Hakim ◽  
Hui-Huang Hsu ◽  
Stanislav Vladimirovich Klimenko ◽  
Timothy K. Shih
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Dynamic Gesture Recognition
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