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 Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

2019 ◽  
Vol 9 (3) ◽  
pp. 528 ◽  
Author(s):  
Fahn Chin-Shyurng ◽  
Shih-En Lee ◽  
Meng-Luen Wu
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Recognition System ◽  
Experimental Result ◽  
Depth Camera ◽  
Time Warping ◽  
Skeleton Model ◽  
Time Dynamic ◽  
Dynamic Time

Gesture recognition is a human−computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.

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.

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
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