Journal ArticleDOI
Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques
TLDR
This system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application.Abstract:
This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture.read more
Citations
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Journal ArticleDOI
Soli: ubiquitous gesture sensing with millimeter wave radar
Jaime Lien,Nicholas Gillian,M. Emre Karagozler,Amihood Patrick M,Carsten Schwesig,Erik M. Olson,Hakim Kader Bhai Raja,Ivan Poupyrev +7 more
TL;DR: It is demonstrated that Soli can be used for robust gesture recognition and can track gestures with sub-millimeter accuracy, running at over 10,000 frames per second on embedded hardware.
Proceedings ArticleDOI
Hand gesture recognition with 3D convolutional neural networks
TL;DR: An algorithm for drivers' hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks using spatio-temporal data augmentation for more effective training and to reduce potential overfitting.
Journal ArticleDOI
A review of hand gesture and sign language recognition techniques
TL;DR: A thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research, suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification.
Book ChapterDOI
Multi-class Open Set Recognition Using Probability of Inclusion
TL;DR: The problem is formulated as one of modeling positive training data at the decision boundary, where the statistical extreme value theory can be invoked, and a new algorithm called the P I -SVM is introduced for estimating the unnormalized posterior probability of class inclusion.
Journal ArticleDOI
Camera as the instrument: the rising trend of vision based measurement
TL;DR: An overview of vision-based measurement (VBM), its various components, and uncertainty in the correct IM (instrumentation and measurement) metrological perspective is given.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.