摘要
针对智能视频监控中运动目标的检测,提出了一种基于字典学习的背景建模方法.结合时空域信息对视频中的每个位置进行字典学习来描述场景信息.利用背景频繁出现的特性,将字典中的词划分成描述背景的词和描述前景的词.用字典表达对应位置的结构,并根据字典中贡献最大词的属性对当前位置进行背景判断.根据判断的结果对字典进行实时更新.在公共的视频数据库上与传统的背景建模方法相比较,所提方法可以较好地检测出前景目标.
To detect the moving objects in the intelligence video surveillance systems, a method of background subtraction was proposed. The proposed method learned a dictionary for each position of the video by using the temporal-spatial information of the local region. Once the dictionaries were built, the information of the corresponding positions in the next frame could be expressed by the dic- tionaries. In this study, it was supposed that the background had a high occurrence frequency. The words of each dictionary were divided into two classes according to the hypothesis. One class was used to describe the background, while the other was employed to express the foreground. The correspond- ing position was judged to be the background or the foreground according to the word that had made the best contribution to the corresponding pixel. The dictionary was updated after the judgment. The proposed method was performed on the public videos against three state-of-the-art algorithms. The experimental results show the superiority of the proposed method.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第9期28-31,共4页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61271328)
广东省教育部产学研结合项目(2011B090400608)
关键词
背景建模
字典学习
视频监控
K均值
词
background subtraction
dictionary learning
video surveillance
K-means
words
作者简介
桑农（1968-），男，教授，E—mail：nsang@hust．edu．cn．
