scholarly journals An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hong-Min Zhu ◽  
Chi-Man Pun
Keyword(s):  
Template Matching ◽  
Tracking System ◽  
Recognition System ◽  
Appearance Model ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Motion Trajectory ◽  
Hand Motion ◽  
Target Hand ◽  
Gesture Tracking

We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories. First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence. Then the hand appearance model is constructed from its surrounding superpixels. By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory. Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier. Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set.

scholarly journals Hand Gesture Recognition System using Deep Learning

2019 ◽  
Vol 8 (6S) ◽  
pp. 240-245
Keyword(s):  
Deep Learning ◽  
Recognition System ◽  
Hand Gesture ◽  
Characteristic Method ◽  
Wide Scope ◽  
Hand Motion ◽  
Wide Range ◽  
Hand Signal ◽  
Unique Signal ◽  
Human Motions

Hand motion acknowledgment is a characteristic method for human PC association and a zone of dynamic research in PC vision and AI. This is a zone with a wide range of conceivable applications, giving clients an easier and increasingly normal approach to speak with robots/frameworks interfaces, without the requirement for additional gadgets. Along these lines, the essential objective of signal acknowledgment explore connected to Human-Computer Interaction (HCI) is to make frameworks, which can distinguish explicit human motions and use them to pass on data or controlling gadgets. For that, vision-based hand signal interfaces require quick and incredibly strong hand discovery, and motion acknowledgment continuously. This paper introduces an answer, sufficiently nonexclusive, with the assistance of deep learning, permitting its application in a wide scope of human-PC interfaces, for ongoing motion acknowledgment. Investigations did demonstrated that the framework had the capacity to accomplish a precision of 99.4% as far as hand act acknowledgment and a normal exactness of 93.72% as far as unique signal acknowledgment.

An Adaptive KLT Algorithm for Hand Gesture Tracking

2013 ◽  
Vol 706-708 ◽  
pp. 623-628
Author(s):  
Huang Xin
Keyword(s):  
Human Computer Interaction ◽  
State Of The Art ◽  
Tracking System ◽  
Adaptive Method ◽  
Experimental Results ◽  
Hand Gesture ◽  
Feature Points ◽  
Tracking Algorithms ◽  
Gesture Tracking ◽  
Conventional Methods

With the development of human-computer interaction technology, hand gesture is widely investigated recently for its natural and convenient properties. In view of the disadvantage of the existing tracking algorithms for the hand gesture, a novel adaptive method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategy based on confidence is utilized properly. However, because the hand is non-rigid, its shape often changes, which easily leads to tracking failure for the reduction of features. In order to solve the problem, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.

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 Virtual Block Augmented Reality Game Using Freehand Gesture Interaction

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Cik Suhaimi Yusof ◽  
Ajune Wanis Ismail
Keyword(s):  
Augmented Reality ◽  
Tracking System ◽  
Recognition System ◽  
Hand Gesture ◽  
Leap Motion ◽  
Gesture Interaction ◽  
Hand Gesture Interaction ◽  
Augmented Reality Game ◽  
World Environment ◽  
Real Hand

Augmented Reality (AR) manages to bring a virtual environment into a real-world environment seamlessly. As AR has been recognised as advancing technology, AR brings future changes to the learning process. The goal of this study is to use freehand gestures to create a virtual block game in AR. First of all, the stages of this study are to explore block games and freehand movements by using Leap Motion. Secondly, the design and development of Leap Motion virtual block games, and thirdly, the implementation of free-hand gesture interaction virtual block games. The paper explains about virtual blocks AR game using freehand gesture. AR tracking system with real hand gesture recognition system is merged to execute the freehand gesture. A prototype virtual block has been described in this paper. The paper ends with the conclusion and future works.

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