Abstract
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.
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Liu, H., Cheng, J., Lu, H. (2010). Abnormal Change Detection of Image Quality Metric Series Using Diffusion Process and Stopping Time Theory . In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_25
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DOI: https://doi.org/10.1007/978-3-642-15702-8_25
Publisher Name: Springer, Berlin, Heidelberg
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