scholarly journals Hand Motion and Posture Recognition in a Network of Calibrated Cameras

2017 ◽  
Vol 2017 ◽  
pp. 1-25 ◽  
Author(s):  
Jingya Wang ◽  
Shahram Payandeh
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Motion Trajectory ◽  
Hand Posture ◽  
Scale Invariant ◽  
Hand Motion ◽  
Hand Posture Recognition ◽  
Posture Recognition

This paper presents a vision-based approach for hand gesture recognition which combines both trajectory and hand posture recognition. The hand area is segmented by fixed-range CbCr from cluttered and moving backgrounds and tracked by Kalman Filter. With the tracking results of two calibrated cameras, the 3D hand motion trajectory can be reconstructed. It is then modeled by dynamic movement primitives and a support vector machine is trained for trajectory recognition. Scale-invariant feature transform is employed to extract features on segmented hand postures, and a novel strategy for hand posture recognition is proposed. A gesture vector is introduced to recognize hand gesture as an entirety which combines the recognition results of motion trajectory and hand postures where a support vector machine is trained for gesture recognition based on gesture vectors.

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

scholarly journals HAND GESTURE RECOGNITION FOR INDONESIAN SIGN LANGUAGE INTERPRETER SYSTEM WITH MYO ARMBAND USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 7 (2) ◽  
pp. 164
Author(s):  
Aditiya Anwar ◽  
Achmad Basuki ◽  
Riyanto Sigit
Keyword(s):  
Support Vector Machine ◽  
Sign Language ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Deaf People ◽  
Hand Gestures ◽  
Moment Invariant ◽  
Myo Armband

<p><em>Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning.  In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. </em><em>This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant</em></p>

An Effective Trajectory Segmentation and Processing Method for Hand Motion Based Shape Conceptualization

2006 ◽  
Author(s):  
Edit Varga ◽  
Imre Horva´th ◽  
Zolta´n Rusa´k
Keyword(s):  
Motion Detection ◽  
Future Research ◽  
Hand Posture ◽  
Computer Algorithms ◽  
Hand Motion ◽  
Hand Posture Recognition ◽  
Posture Recognition ◽  
The Individual ◽  
Visualization Techniques ◽  
Trajectory Segmentation

Efficient support of conceptual design requires dedicated computer-based systems that feature new kinds of interaction and visualization techniques. As input means for this kind of systems, various modalities have been considered. Hand motions have been found to be especially efficient at describing shapes and expressing shape related operations directly in the 3D space. Therefore a formal hand motion language (HML) has earlier been developed by the authors. Computer interpretation of HML is however challenging not only because of the technological complexity of the problem, but also because of the need for real-time computation. Our hypothesis has been that the HML interpretation problem can be reduced to motion detection, trajectory segmentation, hand posture recognition, and command mapping sub-problems. The objective of trajectory segmentation is to find the non-transient parts of the hand motion that can be mapped to the words of HML. In this paper we propose a method which combines trajectory segmentation and hand posture recognition. Based on the postural information that is conveyed by the individual frames of the recorded motion, the beginning and the end of the meaningful segments are identified. In addition, the spatial and geometric information related to the formal HML words is also gathered. These pieces of information are combined in order to reconstruct and visualize the control commands in the shape conceptualization system. The current results shows that the necessary computer algorithms are fast enough and do not impose restrictions on the process of hand motion interpretation. Future research will concentrate on the integration of hand motion detection and reconstruction with visualization and manipulation of shape concepts in a fully volumetric imaging environment.

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