Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well while reconstructing anomalies poorly. However, even with only normal data training, the AEs often reconstruct anomalies well, which depletes their anomaly detection performance. To alleviate this issue, we propose a simple yet efficient framework for video anomaly detection. The pseudo anomaly samples are introduced, which are synthesized from only normal data by embedding random mask tokens without extra data processing. We also propose a normalcy consistency training strategy that encourages the AEs to better learn the regular knowledge from normal and corresponding pseudo anomaly data. This way, the AEs learn more distinct reconstruction boundaries between normal and abnormal data, resulting in superior anomaly discrimination capability. Experimental results demonstrate the effectiveness of the proposed method.

翻译：由于训练中异常样本的可用性有限，视频异常检测通常被视为单分类问题。许多主流方法基于自编码器（AE）的重建差异开展研究，假设自编码器能良好重建正常数据而难以重建异常。然而，即使仅使用正常数据训练，自编码器往往也能较好地重建异常，这削弱了其异常检测性能。为缓解这一问题，我们提出一个简单而高效的视频异常检测框架。该框架引入通过仅从正常数据嵌入随机掩码标记合成的伪异常样本，无需额外数据处理。我们还提出一种正常性一致性训练策略，促使自编码器更有效地从正常数据和对应的伪异常数据中学习常规知识。通过这种方式，自编码器学习到更清晰的正常与异常数据间的重建边界，从而获得更优的异常判别能力。实验结果证明了所提方法的有效性。