ABSTRACT
There are basically two types of approaches for hand gesture recognition: vision-based approaches and data glove methods. In the study we will be focusing our attention on vision-based approaches. Why vision-based hand gesture recognition systems? Vision-based hand gesture recognition systems provide a simpler and more intuitive way of communication between a human and a computer. Using visual input in this context makes it possible to communicate remotely with computerized equipment, without the need for physical contact. The main objective of this work is to study and implement solutions that can be generic enough, with the help of machine learning algorithms, allowing its application in a wide range of humancomputer interfaces, for online gesture recognition. In pursuit of this, we intend to use a depth sensor
1 INTRODUCTION
Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research, is to create a system, which can identify specific gestures and use them to convey information or for device control. For that, gestures need to be modelled in the spatial and temporal domains, where a hand posture is the static structure of the hand and a gesture is the dynamic movement of the hand. Being handpose one of the most important communication tools in human’s daily life, and with the continuous advances of image and video processing techniques, research on human-machine interaction through gesture recognition led to the use of such technology in a very broad range of applications, like touch screens, video game consoles, virtual reality, medical applications, etc. There are areas where this trend is an asset, as for example in the
camera to detect and extract hand information (hand features), for gesture classification. With the implemented solutions we intend to develop an integrated vision-based hand gesture recognition system, for offline training of static and dynamic hand gestures, in order to create models, that can be used for online classification of user commands.
