Hidden Markov Model gesture recognition



Online, Interactive Learning of Gestures for Human/Robot Interfaces
Abstract: We have developed a gesture recognition system, based on Hidden Markov Models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 different gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language...

Hidden Markov Model for Gesture Recognition
Abstract: This report presents a method for developing a gesture-based system using a multi-dimensional hidden Markov model (HMM). Instead of using geometric features, gestures are converted into sequential symbols. HMMs are employed to represent the gestures and their parme- ters are learned from the training data. Based on “the most likely performance” criterion, the gestures can be recognized through evaluating the trained HMMs. We have developed a prototype system to demonstrate the feasibility of the proposed method. The system achieved 99.78% accuracy for an isolated recognition task with nine gestures. Encouraging results were also obtained from experiments of continuous gesture recognition. The proposed method is applicable to any gesture represented by a multi-dimensional signal, and will be a valuable tool in telerobotics and human computer interfaces.

Recognition of Space-Time Hand-Gestures using Hidden Markov Model
Abstract: The rapidly growing interest in interactive threedimensional (3D) computer environments highly recommend the hand gesture as one of their interaction modalities. Among several factors constituting a hand gesture, hand movement pattern is spatiotemporally variable and informative, but its automatic recognition is not trivial. In this paper, we describe a hidden Markov(HMM)-based method for recognizing the space-time hand movement pattern. HMM models the spatial variance and the time-scale...

High Performance Real-Time Gesture Recognition Using Hidden Markov Models Abstract: . An advanced real-time system for gesture recognition is presented, which is able to recognize complex dynamic gestures, such as "hand waving", "spin", "pointing", and "head moving". The recognition is based on global motion features, extracted from each difference image of the image sequence. The system uses Hidden Markov Models (HMMs) as statistical classifier. These HMMs are trained on a database of 24 isolated gestures, performed by 14 different people. With the use of global motion ...

Recognition of Head Gestures Using Hidden Markov Models
Abstract: This paper explores the use of Hidden Markov Models (HMMs) for the recognition of head gestures. A gesture corresponds to a particular pattern of head movement. The facial plane is tracked using a parameterized model and the temporal sequence of three image rotation parameters are used to describe four gestures. A dynamic vector quantization scheme was implemented to transform the parameters into suitable input data for the HMMs. Each model was trained by the iterative Baum-Welch procedure...

Visual Recognition of American Sign Language Using Hidden Markov Models
Abstract: Hidden Markov models (HMM's) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. We describe an HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers. 1 Introduction There has been a resurging interest in...

Hidden Markov Model Based Continuous Online Gesture Recognition
Abstract: This paper presents the extension of an existing visionbased gesture recognition system using Hidden Markov Models (HMMs). Several improvements have been carried out in order to increase the capabilities and the functionality of the system. These improvements include positionindependent recognition, rejection of unknown gestures, and continuous online recognition of spontaneous gestures. We show that especially the latter requirement is highly complicated and demanding, if we allow the user to...

Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
Abstract: Given a source of time series data, there is often utility in determining whether there are qualitatively different regimes in the data and in characterizing those regimes. Hidden Markov models (HMMs) have been demonstrated empirically to be capable of modeling the structure of the generative processes underlying a wide variety of real-world time series. However, existing tools for inducing HMMs from data assume that all of the data are to be fit by a single monolithic model. This paper.

Nonlinear Parametric Hidden Markov Models
Abstract: In previous work (see TR-421), we extended the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the states of the HMM. Development of the parametric HMM was motivated by the task of simultaneoiusly recognizing and interpreting gestures that exhibit meaningful variation. With standard HMMs, such global variation confounds the recognition process. In this paper we extend the parametric HMM approach to handle nonlinear (non-analytic) dependencies of the output distributions on the parameter of interest. We show a generalized expectation-maximization (GEM) algorithm for training the parametric HMM and a GEM algorithm to simultaneously recognize the gesture and estimate the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural azimuth/elevation parameterization of pointing direction.

Recognition and Interpretation of Parametric Gesture
Abstract: A new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a meaningful variation; one example is a point gesture where the important parameter is direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the states of the HMM. Using a linear model to derive the theory, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing, the parametric HMM simultaneously recognizes the gesture and estimates the quantifying parameters. Using visually-derived and directly measured 3-dimensional hand position measurements as input, we present results on two different movements --- a size gesture and a point gesture --- and show robustness with respect to noise in the input features

Recognition of Space-Time Hand-Gestures using Hidden Markov Model
Abstract: The rapidly growing interest in interactive threedimensional (3D) computer environments highly recommend the hand gesture as one of their interaction modalities. Among several factors constituting a hand gesture, hand movement pattern is spatiotemporally variable and informative, but its automatic recognition is not trivial. In this paper, we describe a hidden Markov(HMM)-based method for recognizing the space-time hand movement pattern. HMM models the spatial variance and the time-scale

Adaptive Models for Gesture Recognition
Abstract: Tomorrow’s ubiquitous computing environments will go beyond the keyboard, mouse and monitor paradigm ofin teraction and will require the automatic interpretation of human motion using a variety ofsensors including video cameras. I present several techniques for human motion recognition that are inspired by observations on human gesture, the class ofco mmunicative human movement. Typically, gesture recognition systems are unable to handle systematic variation in the input signal, and so are too brittle to be applied successfully in many real-world situations. To address this problem, I present modeling and recognition techniques to adapt gesture models to the situation at hand. A number ofsystems and frameworks that use adaptive gesture models are presented. First, the parametric hidden Markov model (PHMM) addresses the representation and recognition ofgesture families, to extract how a gesture is executed. Second, strong temporal models drawn from natural gesture theory are exploited to segment two kinds ofnatural gestures from video sequences. Third, a realtime computer vision system learns gesture models online from time-varying context. Fourth, a realtime computer vision system employs hybrid Bayesian networks to unify and extend the previous approaches, as well as point the way for future work.

Realtime Online Adaptive Gesture Recognition
Abstract: We introduce an online adaptive algorithm for learning gesture models. By learning gesture models in an online fashion, the gesture recognition process is made more robust, and the need to train on a large training ensemble is obviated. Hidden Markov models are used to represent the spatial and temporal structure of the gesture. The usual output probabilitydistributions—typically representing appearance — are trained at runtime exploiting the temporal structure (Markov model) that is either trained off-line or is explicitly hand-coded. In the early stages of runtime adaptation, contextual information derived from the application is used to bias the expectation as to which Markov state the system is in at any given time. We describe the Watch and Learn system, a computer vision system which is able to learn simple gestures online for interactive control.



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