In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises from the inherent ambiguity in temporal annotations of abnormal occurrences. Inspired by the statistical insight that temporal features of abnormal events often exhibit outlier characteristics, we propose a novel method, BN-WVAD, which incorporates BatchNorm into WVAD. In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos. The proposed DFM criterion is also discriminative for anomaly recognition and more resilient to label noise, serving as the additional anomaly score to amend the prediction of the anomaly classifier that is susceptible to noisy labels. Moreover, a batch-level selection strategy is devised to filter more abnormal snippets in videos where more abnormal events occur. The proposed BN-WVAD model demonstrates state-of-the-art performance on UCF-Crime with an AUC of 87.24%, and XD-Violence, where AP reaches up to 84.93%. Our code implementation is accessible at https://github.com/cool-xuan/BN-WVAD.

在弱监督视频异常检测（WVAD）中，我们提出了一种新方法BN-WVAD，它将BatchNorm技术应用于WVAD中，利用来自BatchNorm的均值向量差异（DFM）作为可靠的异常判据来区分异常视频中的潜在异常片段，并且使用DFM作为额外的异常分数修正对异常分类器的预测，同时设计了一种批级选择策略以过滤更多异常片段，实验结果表明，BN-WVAD模型在UCF-Crime数据集上达到了87.24%的AUC， 在XD-Violence数据集上达到了84.93%的AP。

基于BatchNorm的弱监督视频异常检测