Détection et Reconnaissance des Gestes Emblématiques

Session "Atelier IHMA"National audienceDans cette contribution, nous présentons un système de reconnaissance en ligne de gestes emblématiques et son utilisation pour le contrôle d'un robot mobile. Ce système comporte quatre sous-systèmes : un premier qui permet de détecter la personne et d'extraire les mouvements de la partie supérieure de cette personne. Un second, permet d'isoler les mouvements, une troisième permet de reconnaître un des mouvements appris a priori. Enfin le dernier module, traduit les mouvements reconnus en termes de contrôle d'un robot mobile à roues. Dans notre approche, nous avons surtout traité du problème de la généralisation : faire l'apprentissage sur une base réduite de personnes et utiliser cette connaissance pour reconnaître n'importe quelle personne, indépendamment de sa morphologie, de son âge, de son sexe et de son positionnement par rapport au capteur. Nous détaillons les résultats obtenus pour la reconnaissance ainsi que l'utilisation du système dans des scenarios de contrôle d'un robot

Towards Developing an Effective Hand Gesture Recognition System for Human Computer Interaction: A Literature Survey

Gesture recognition is a mathematical analysis of movement of body parts (hand / face) done with the help of computing device. It helps computers to understand human body language and build a more powerful link between humans and machines. Many research works are developed in the field of hand gesture recognition. Each works have achieved different recognition accuracies with different hand gesture datasets, however most of the firms are having insufficient insight to develop necessary achievements to meet their development in real time datasets. Under such circumstances, it is very essential to have a complete knowledge of recognition methods of hand gesture recognition, its strength and weakness and the development criteria as well. Lots of reports declare its work to be better but a complete relative analysis is lacking in these works. In this paper, we provide a study of representative techniques for hand gesture recognition, recognition methods and also presented a brief introduction about hand gesture recognition. The main objective of this work is to highlight the position of various recognition techniqueswhich can indirectly help in developing new techniques for solving the issues in the hand gesture recognition systems. Moreover we present a concise description about the hand gesture recognition systems recognition methods and the instructions for future research

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

A review of temporal aspects of hand gesture analysis applied to discourse analysis and natural conversation

Lately, there has been a\ud n increasing\ud interest in hand gesture analysis systems. Recent works have employed\ud pat\ud tern recognition techniques and have focused on the development of systems with more natural user\ud interfaces. These systems may use gestures to control interfaces or recognize sign language gestures\ud , which\ud can provide systems with multimodal interaction; o\ud r consist in multimodal tools to help psycholinguists to\ud understand new aspects of discourse analysis and to automate laborious tasks.\ud Gestures are characterized\ud by several aspects, mainly by movements\ud and sequence of postures\ud . Since data referring to move\ud ments\ud or\ud sequences\ud carry temporal information\ud , t\ud his paper presents a\ud literature\ud review\ud about\ud temporal aspects of\ud hand gesture analysis, focusing on applications related to natural conversation and psycholinguistic\ud analysis, using Systematic Literature Revi\ud ew methodology. In our results, we organized works according to\ud type of analysis, methods, highlighting the use of Machine Learning techniques, and applications.FAPESP 2011/04608-

Simultaneous Gesture Segmentation and Recognition based on Forward Spotting Accumulative HMMs

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