Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications

Sensors

Carlos R. del-Blanco¹  Tomás Mantecón¹  Massimo Camplani¹  Fernando Jaureguizar¹  Luis Salgado¹ 

[1] Grupo de Tratamiento de Imágenes, E.T.S.I de Telecomunicación, Universidad Politécnica de Ma Avenida Complutense 30, Madrid 28040, Spain; E-Mails:

关键词: depth sensors;    foreground segmentation;    video surveillance;    Bayesian network;   

DOI  :  10.3390/s140201961

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.