Recently, due to its widespread applications in public safety, anomaly detection in crowd scenes has become a hot topic. Some deep-learning-based methods attain significant achievements in this field. Nevertheless, most of them suffer from over-fitting to some extent because of scarce data, which are usually abrupt and low-frequency in the real world. To remedy the above problem, this paper firstly develops a synthetic anomaly event generating system, which could simulate typical specific abnormal events. By utilizing this system, a large synthetic, diverse anomaly event dataset is built, which contains 2,149 video sequences. After getting the dataset, a 3D CNN is designed to detect the abnormal types at the video level. However, we find that there are obvious domain differences (also named as “domain gap/shifts”) between synthetic videos and real-world data, which results in performance degradation when applying the model to the real world. Thus, this paper further proposes a cyclic 3D GAN for domain adaption to reduce the domain gap, which translates the synthetic data to the photorealistic video sequences. Then the detection model is trained on the translated data and it can perform well in the real data. Experimental results illustrate that the proposed method outperforms these baselines for the domain adaptation anomaly detection.

近年来，由于其在公共安全中的广泛应用，人群场景中的异常检测已成为热门话题。一些基于深度学习的方法在该领域取得了显著成就。然而，由于缺乏数据，它们中的大多数人在某种程度上遭受了过度拟合的困扰，而这些数据在现实世界中通常是突变且低频的。为了解决上述问题，本文首先开发了一种可以模拟典型的特定异常事件的综合异常事件发生系统。通过使用该系统，建立了一个大型的综合，多样的异常事件数据集，其中包含2149个视频序列。获取数据集后，将设计3D CNN以在视频级别检测异常类型。然而，我们发现合成视频与现实世界数据之间存在明显的领域差异（也称为“领域差距/转移”），这在将模型应用于现实世界时会导致性能下降。因此，本文进一步提出了一种用于域自适应的循环3D GAN，以减少域间隙，从而将合成数据转换为逼真的视频序列。然后，在翻译后的数据上训练检测模型，该模型可以在实际数据中表现良好。实验结果表明，所提出的方法在域自适应异常检测方面优于这些基线。它将合成数据转换为逼真的视频序列。然后，在翻译后的数据上训练检测模型，该模型可以在实际数据中表现良好。实验结果表明，所提出的方法在域自适应异常检测方面优于这些基线。它将合成数据转换为逼真的视频序列。然后，在翻译后的数据上训练检测模型，该模型可以在实际数据中表现良好。实验结果表明，所提出的方法在域自适应异常检测方面优于这些基线。