In this paper, we study challenging anomaly detections in streaming videos under fully unsupervised settings. Unsupervised unmasking methods [12] have recently been applied to anomaly detection; however, the theoretical understanding of it is still limited. Aiming to understand and improve this method, we propose a novel perspective to establish the connection between the heuristic unmasking procedure and multiple classifier two sample tests (MC2ST) in statistical machine leaning. Based on our

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... nalysis of the testing power of MC2ST, we present a history sampling method to increase the testing power as well as to improve the performance on video anomaly detection. We also offer a new frame-level motion feature that has better representation and generalization ability, and obtain improvement on several video benchmark datasets. The code could be found at https://github.com/MYusha/Video-Anomaly-Detection.