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Abnormal Change Detection of Image Quality Metric Series Using Diffusion Process and Stopping Time Theory

机译:基于扩散过程和停止时间理论的图像质量指标序列异常变化检测

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To evaluate and monitor the Image Quality (IQ) change of a surveillance sequence for video analysis, a diffusion process and stopping time theory based model is presented in this paper because they can describe the uncertainty of an actual stochastic series rationally. First, we calculate the IQ metric for each frame. Then we connect all these discrete data together to form an Image Quality Metric Series (IQMS). After that, a non-parametric estimation technique based diffusion process model is used to fit the fluctuation path of the IQMS. Finally, a stopping time based model is employed to detect the abnormal change. Different to the conventional diffusion process method, the function forms of our model are estimated online and affirmed by an evaluation result of the hypothesis test. Comparing with the traditional time series model, such as the ARMA model, extensive experiments have proved that this method is effective and efficient on detecting the abnormal change.
机译:为了评估和监视用于视频分析的监视序列的图像质量(IQ)变化,本文提出了一种基于扩散过程和停止时间理论的模型,因为它们可以合理地描述实际随机序列的不确定性。首先,我们为每个帧计算IQ度量。然后,我们将所有这些离散数据连接在一起,以形成图像质量指标系列(IQMS)。之后,使用基于非参数估计技术的扩散过程模型来拟合IQMS的波动路径。最后,采用基于停止时间的模型来检测异常变化。与常规扩散处理方法不同,我们的模型的功能形式是在线估计的,并由假设检验的评估结果确定。与传统的时间序列模型(如ARMA模型)相比,大量实验证明该方法对于检测异常变化是有效且高效的。

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