摘要
针对安防监控摄像头被树叶遮挡的问题,提出一种基于支持向量机(SVM)的视频树叶遮挡检测算法。该算法利用视频的时域特性,采用累积帧差法实现对视频中疑似树叶区域的分割,提取视频中某一帧图像的整个区域和疑似树叶区域的颜色信息与面积信息作为视频的特征,最后采用支持向量机进行建模并用于视频树叶遮挡的检测。在有限样本前提下,算法准确率能够达到84%。实验结果表明,所提算法对于有树叶遮挡的监控视频能够进行有效识别。
Aiming at the problem that the security surveillance cameras have been hidden by leaves, a leaf occlusion detection algorithm based on Support Vector Machine （SVM） was proposed. The algorithm contains three steps. First, the regions of the leaf existing in the video were segmented. The accumulated frame subtraction method was applied to achieve this purpose. Second, the color and area information of the whole video image and the segmented regions were extracted as the key features. Third, these features were used for modeling and detecting obstacle occlusion by SVM. For all the collected samples, the detection accuracy of this method can reach up to 84%. The experimental results show that the proposed algorithm can detect the leaf occlusion in security surveillance video effectively.
出处
《计算机应用》
CSCD
北大核心
2014年第7期2023-2027,2032,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61375017)
湖北省高等学校优秀中青年科技创新团队计划项目(T201202)
湖北省教育厅科学技术研究计划项目(Q20131108)
关键词
树叶遮挡检测
监控视频
累积帧差
支持向量机
颜色特征
leaf occlusion detection
surveillance video
accumulated inter-frame subtraction
Support Vector Machine（SVM）
color feature
