Real-time capable system for hand gesture recognition Using hidden Markov models in stereo color image sequences

This paper proposes a system to recognize the alphabets and numbers in real time from color image sequences by the motion trajectory of a single hand using Hidden Markov Models (HMM). Our system is based on three main stages; automatic segmentation and preprocessing of the hand regions, feature extraction and classification. In automatic segmentation and preprocessing stage, YCbCr color space and depth information are used to detect hands and face in connection with morphological operation where Gaussian Mixture Model (GMM) is used for computing the skin probability. After the hand is detected and the centroid point of the hand region is determined, the tracking will take place in the further steps to determine the hand motion trajectory by using a search area around the hand region. In the feature extraction stage, the orientation is determined between two consecutive points from hand motion trajectory and then it is quantized to give a discrete vector that is used as input to HMM. The final stage so-called classification, Baum-Welch algorithm (BW) is used to do a full train for HMM parameters. The gesture of alphabets and numbers is recognized by using Left-Right Banded model (LRB) in conjunction with Forward algorithm. In our experiment, 720 trained gestures are used for training and also 360 tested gestures for testing. Our system recognizes the alphabets from A to Z and numbers from 0 to 9 and achieves an average recognition rate of 94.72%

Multi-modal human gesture recognition combining dynamic programming and probabilistic methods

In this M. Sc. Thesis, we deal with the problem of Human Gesture Recognition using Human Behavior Analysis technologies. In particular, we apply the proposed methodologies in both health care and social applications. In these contexts, gestures are usually performed in a natural way, producing a high variability between the Human Poses that belong to them. This fact makes Human Gesture Recognition a very challenging task, as well as their generalization on developing technologies for Human Behavior Analysis. In order to tackle with the complete framework for Human Gesture Recognition, we split the process in three main goals: Computing multi-modal feature spaces, probabilistic modelling of gestures, and clustering of Human Poses for Sub-Gesture representation. Each of these goals implicitly includes different challenging problems, which are interconnected and faced by three presented approaches: Bag-of-Visual-and-Depth-Words, Probabilistic-Based Dynamic Time Warping, and Sub-Gesture Representation. The methodologies of each of these approaches are explained in detail in the next sections. We have validated the presented approaches on different public and designed data sets, showing high performance and the viability of using our methods for real Human Behavior Analysis systems and applications. Finally, we show a summary of different related applications currently in development, as well as both conclusions and future trends of research

Hand gesture spotting and recognition using HMMs and CRFs in color image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010von Mahmoud Othman Selim Mahmoud Elmezai

Sistema de reconocimiento de acciones mediante cámaras con detección de profundidad

El campo de monitorización y reconocimiento de movimientos es el centro de muchas investigaciones, dadas sus múltiples aplicaciones. Mediante el estudio de los sistemas actuales pueden observarse limitaciones y otros aspectos de mejora. Es posible aplicar nuevas técnicas de forma que se haga uso de tecnología comercial actual para mejorar el rendimiento de dichos sistemas de reconocimiento. De esta forma, este trabajo presenta un estudio del estado del arte en la monitorización y reconocimiento mediante información de profundidad, en el que se destacan las fortalezas y debilidades de los paradigmas actuales. A partir de este análisis, se propone un esquema de reconocimiento con capacidad de mejorar los sistemas actuales. La implementación consiste en un ToolBox de MatLab® con distintas funcionalidades. Sobre este se realizan una serie de experimentos y se discuten los resultados obtenidos, de forma que pueda trazarse un plan para futuros desarrollos que hagan uso de este estudio.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione