CAJ | 학술논문

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。
침대지능시빈감공적수구，제출일충무감독학습적이상행위검측방법。수선，채용혼합고사모형건모제취출운동목표，대운동구역진행표기；연후제취운동구역내적광류신식，장기귀일화성특정구진，병건립실시경신적특정구진관측서렬；최후이용이유주성분분석(2DPCA)적중구원리대관측서렬진행분석，근거중구특정구진여원특정구진적능량비래판단시부존재이상행위。기우불동수거고하적시빈서렬실험결과험증료소제방법적유효성。
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.