Video Based Recognition Of Hand Gestures By Neural Networks For The Control Of Sound And Music
Paul Modler, Tony Myatt · Zenodo (CERN European Organization for Nuclear Research) · 2008
recent years video based analysis of human motion gained increased interest, which for a large part is due to the ongoing rapid developments of computer and camera hardware, such as increased CPU power, fast and modular interfaces and high quality image digitisation. A similar important role plays the development of powerful approaches for the analysis of visual data from video sources. In computer music this development is reflected by a row of applications approaching the analysis of video and image data for gestural control of music and sound such as Eyesweb, Jitter, CV ((1,(2), (3)). Recognition and interpretation of hand movements is of great interest both in the areas of music and software engineering ((4), (5), (6)). In this demo an approach is presented for the control of music and sound parameters through hand gestures, which are recognised by an artificial neural network (ANN). The recognition network was trained with appearancebased features extracted from image sequences of a video camera.