摘要
针对智能视频监控的需求,提出一种无监督学习的异常行为检测方法。首先,采用混合高斯模型建模提取出运动目标,对运动区域进行标记;然后提取运动区域内的光流信息,将其归一化成特征矩阵,并建立实时更新的特征矩阵观测序列;最后利用二维主成分分析(2DPCA)的重构原理对观测序列进行分析,根据重构特征矩阵与原特征矩阵的能量比来判断是否存在异常行为。基于不同数据库下的视频序列实验结果验证了所提方法的有效性。
In order to meet the needs of intelligent video surveillance, an unsupervised abnormal detecting algorithm was proposed. Firstly, model of mixture of Gaussians was used to extract the motion area, and the motion area was labeled. Then, observation sequence updated in real-time of feature matrix was established by the optical flow features obtained from labeled area which was normalized to the feature matrix. Finally, applying reconstruction works of two-dimensional principal component analysis on the sequence, abnormal behavior can be detected according to the energy ratio between the recovered feature matrix and original feature matrix. Experiments were conducted on various video datasets, which shows the effectiveness of the proposed method.
出处
《光电工程》
CAS
CSCD
北大核心
2014年第3期43-48,共6页
Opto-Electronic Engineering
基金
国家自然科学基金(60574051)
江苏省产学研联合创新资金-前瞻性联合研究项目(BY2012067)
关键词
异常行为检测
光流特征
二维主成分分析
无监督学习
abnormal behavior detection
optical flow feature
two-dimensional principal component analysis
unsupervised learning
