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As most anomaly detection methods generally ignore the impact of video quality fluctuations in actual scenarios on their performance. Firstly, public datasets from the field of video anomaly detection were selected, and a distortion dataset for anomaly detection by artificially introducing common distortions found in surveillance videos through simulation methods was constructed. This paper systematically analyzes the impact of video quality fluctuations on the performance of four classic video anomaly detection algorithms. And the experimental results show that the degradation of surveillance data quality have a negative effect on the effectiveness of the video anomaly detection algorithm. Moreover, the impact of different distortion types and different degrees of distortion on the algorithm is different, as well as, Gaussian noise distortion and over brightness distortion have a greater impact on the algorithm. [ABSTRACT FROM AUTHOR]

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