华东交通大学学报2012，Vol.29Issue(1)：43-47,5.
基于压缩感知和字典学习的背景差分法
Background Subtraction Based on Sparse Representation and Dictionary Learning
摘要
Abstract
In this paper, we propose a CS-based background subtraction approach based on the theory of sparse representation and dictionary learning, to handle sudden and gradual background changes and the redundancy of excessive image data and the interference of prospect. This method gets their data dictionary according to the video stream and establishes the background model based on the theory of dictionary learning and sparse representation to effectively reduce data redundancy. Then, the moving objects correctly depending on the intensity of the target and its neighbors are segmented so as to rule out interference of the foreground. Finally, the problem of sudden and gradual background changes is solved through the update algorithm of data dictionary. Experiments show that this method is feasible.关键词
稀疏表示/字典学习/背景差分/前景分割Key words
sparse representation/dictionary learning/background subtraction/foreground segmentation分类
信息技术与安全科学引用本文复制引用
郭厚焜,吴峰,黄萍..基于压缩感知和字典学习的背景差分法[J].华东交通大学学报,2012,29(1):43-47,5.基金项目
江西省研究生创新专项基金项目(YC2011-X013) （YC2011-X013）
