Interleaving One-Class and Weakly-Supervised Models with Adaptive
  Thresholding for Unsupervised Video Anomaly Detection
Yongwei Nie Hao Huang Chengjiang Long Qing Zhang Pradipta Maji Hongmin Cai
Without human annotations, a typical Unsupervised Video Anomaly Detection
(UVAD) method needs to train two models that generate pseudo labels for each
other. In previous work, the two models are closely entangled with each other,
and it is not known how to upgrade their method without modifying their
training framework significantly. Second, previous work usually adopts fixed
thresholding to obtain pseudo labels, however the user-specified threshold is
not reliable which inevitably introduces errors into the training process. To
alleviate these two problems, we propose a novel interleaved framework that
alternately trains a One-Class Classification (OCC) model and a
Weakly-Supervised (WS) model for UVAD. The OCC or WS models in our method can
be easily replaced with other OCC or WS models, which facilitates our method to
upgrade with the most recent developments in both fields. For handling the
fixed thresholding problem, we break through the conventional cognitive
boundary and propose a weighted OCC model that can be trained on both normal
and abnormal data. We also propose an adaptive mechanism for automatically
finding the optimal threshold for the WS model in a loose to strict manner.
Experiments demonstrate that the proposed UVAD method outperforms previous
approaches.