计算机与现代化Issue(11)：58-63,6.DOI:10.3969/j.issn.1006-2475.2016.11.010
Computer and Modernization
Moving Object Detection Based on Scene Semantic Prior and Global Appearance Consistency
焦玉清 1王文中 1罗斌1
作者信息
- 1. 安徽大学计算机科学与技术学院，安徽 合肥 230601
- 折叠
摘要
Abstract
Moving object detection in dynamic background is a very challenging fundamental problem in video surveillance. This paper presents a robust moving object detection method. First, we develop an effective ViBe algorithm against dynamic back-ground by incorporating the scene prior information that is predefined in initial frame. Then, the global GMM models of fore-ground objects and background are estimated by foreground and background pixels detected by the improved ViBe algorithm. These GMM models are employed to classify every pixel effectively and remove some of the false results. For further alleviating the effects of noises, the superpixel-based refinement is adopted to obtain the final results. The experimental results on the collected video sequence with strongly dynamic background suggest that the method significantly outperforms other moving object detection methods.关键词
动态背景/场景语义先验/ViBe算法/外观一致性/GMM模型Key words
dynamic background/scene semantic prior/ViBe algorithm/appearance consistency/GMM model分类
信息技术与安全科学引用本文复制引用
焦玉清,王文中,罗斌..Computer and Modernization[J].计算机与现代化,2016,(11):58-63,6.
