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Advances in Multimedia
Volume 2017 (2017), Article ID 5179013, 9 pages
Research Article

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

School of Electronic Science & Applied Physics, Hefei University of Technology, Hefei, China

Correspondence should be addressed to Yizhong Yang

Received 26 January 2017; Revised 26 April 2017; Accepted 24 May 2017; Published 18 June 2017

Academic Editor: Deepu Rajan

Copyright © 2017 Yizhong Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.