RESUMEN
Visual hand gesture recognition systems are promising technologies for Human Computer Interaction, as they allow a more immersive and intuitive interaction. Most of these systems are based on the analysis of skeleton information, which is in turn inferred from color, depth, or near-infrared imagery. However, the robust extraction of skeleton information from images is only possible for a subset of hand poses, which restricts the range of gestures that can be recognized. In this paper, a real-time hand gesture recognition system based on a near-infrared device is presented, which directly analyzes the infrared imagery to infer static and dynamic gestures, without using skeleton information. Thus, a much wider range of hand gestures can be recognized in comparison with skeleton-based approaches. To validate the proposed system, a new dataset of near-infrared imagery has been created, from which good results that outperform other state-of-the-art strategies have been obtained.
Asunto(s)
Gestos , Imagen Óptica , Reconocimiento de Normas Patrones Automatizadas , Mano , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen Óptica/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Reconocimiento en PsicologíaRESUMEN
Low-cost systems that can obtain a high-quality foreground segmentation almost independently of the existing illumination conditions for indoor environments are very desirable, especially for security and surveillance applications. In this paper, a novel foreground segmentation algorithm that uses only a Kinect depth sensor is proposed to satisfy the aforementioned system characteristics. This is achieved by combining a mixture of Gaussians-based background subtraction algorithm with a new Bayesian network that robustly predicts the foreground/background regions between consecutive time steps. The Bayesian network explicitly exploits the intrinsic characteristics of the depth data by means of two dynamic models that estimate the spatial and depth evolution of the foreground/background regions. The most remarkable contribution is the depth-based dynamic model that predicts the changes in the foreground depth distribution between consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that the proposed combination of algorithms is able to obtain a more accurate segmentation of the foreground/background than other state-of-the art approaches.
RESUMEN
Low-cost depth cameras, such as Microsoft Kinect, have completely changed the world of human-computer interaction through controller-free gaming applications. Depth data provided by the Kinect sensor presents several noise-related problems that have to be tackled to improve the accuracy of the depth data, thus obtaining more reliable game control platforms and broadening its applicability. In this paper, we present a depth-color fusion strategy for 3-D modeling of indoor scenes with Kinect. Accurate depth and color models of the background elements are iteratively built, and used to detect moving objects in the scene. Kinect depth data is processed with an innovative adaptive joint-bilateral filter that efficiently combines depth and color by analyzing an edge-uncertainty map and the detected foreground regions. Results show that the proposed approach efficiently tackles main Kinect data problems: distance-dependent depth maps, spatial noise, and temporal random fluctuations are dramatically reduced; objects depth boundaries are refined, and nonmeasured depth pixels are interpolated. Moreover, a robust depth and color background model and accurate moving objects silhouette are generated.