As an important and challenging task for intelligent video surveillance systems, video anomaly detection is generally referred to as automatic recognition of video frames that contain abnormal targets, behavior or events. Although it has been widely applied in real scenes, anomaly detection remains a challenging task because of the vague definition of anomaly and the lack of the anomaly samples. Inspired by the widespread application of Generative Adversarial Network (GAN), we propose an end-to-end pipeline called NM-GAN which assembles an encode-decoder reconstruction network and a CNN-based discrimination network in a GAN-like architecture. The generalization ability of the reconstruction network is properly modulated via the adversarial learning around reconstruction error maps and noise maps. Meanwhile, the discrimination network is trained to distinguish anomaly samples from normal samples based on the reconstruction error maps. Finally, the output of the discrimination network is transferred to evaluate anomaly score of the input frame. The thorough proof-of-principle experiments and ablation tests on several popular datasets reveal that the proposed model enhance the generalization ability of the reconstruction network and the distinguishability of the discrimination network significantly. The comparison with the state-of-the-art shows that the proposed NM-GAN model outperforms most competing models in precision and stability.

作为智能视频监视系统的一项重要且具有挑战性的任务，视频异常检测通常称为自动识别包含异常目标，行为或事件的视频帧。尽管它已被广泛应用于真实场景中，但由于对模糊的定义不明确且缺少异常样本，因此异常检测仍然是一项艰巨的任务。受生殖对抗网络（GAN）广泛应用的启发，我们提出了一种称为NM-GAN的端到端管道，该管道在GAN架构中组装了编解码器重构网络和基于CNN的区分网络。通过围绕重建误差图和噪声图的对抗学习，可以适当地调整重建网络的泛化能力。同时，训练辨别网络，以基于重建误差图将异常样本与正常样本区分开。最后，鉴别网络的输出被传送以评估输入帧的异常得分。在几个流行的数据集上进行的彻底的原理验证实验和消融测试表明，该模型显着提高了重建网络的泛化能力和判别网络的可分辨性。与最新技术的比较表明，所提出的NM-GAN模型在精度和稳定性方面优于大多数竞争模型。在几个流行的数据集上进行的彻底的原理验证实验和消融测试表明，该模型显着提高了重建网络的泛化能力和判别网络的可分辨性。与最新技术的比较表明，所提出的NM-GAN模型在精度和稳定性方面优于大多数竞争模型。在几个流行的数据集上进行的彻底的原理验证实验和消融测试表明，该模型显着提高了重建网络的泛化能力和判别网络的可分辨性。与最新技术的比较表明，所提出的NM-GAN模型在精度和稳定性方面优于大多数竞争模型。