Grupo de Tratamiento de Imágenes, E.T.S.I de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain.
Sensors (Basel). 2014 Jan 24;14(2):1961-87. doi: 10.3390/s140201961.
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.
低成本系统，可以获得一个高质量的前景分割几乎独立于现有的照明条件，为室内环境，是非常可取的，特别是对安全和监控应用。在本文中，提出了一种新的前景分割算法，仅使用 Kinect 深度传感器来满足上述系统的特点。这是通过结合混合高斯背景减法算法与一个新的贝叶斯网络，稳健地预测前景/背景区域之间的连续时间步骤。贝叶斯网络明确利用深度数据的内在特性，通过两个动态模型来估计前景/背景区域的空间和深度演变。最显著的贡献是基于深度的动态模型，预测在连续时间步骤之间的前景深度分布的变化。这是一个关键的区别，与可见的图像，其中的前景的颜色/灰度分布通常被认为是恒定的。实验在两个不同的基于深度的数据库上进行，结果表明，该算法的组合能够比其他最先进的方法获得更准确的前景/背景分割。
