Video anomaly detection has gained significant attention in the current intelligent surveillance systems. We propose Deep Residual Spatiotemporal Translation Network (DR-STN), a novel unsupervised Deep Residual conditional Generative Adversarial Network (DR-cGAN) model with an Online Hard Negative Mining (OHNM) approach. The proposed DR-cGAN provides a wider network to learn a mapping from spatial to temporal representations and enhance the perceptual quality of synthesized images from a generator. During DR-cGAN training, we take only the frames of normal events to produce their corresponding dense optical flow. At testing time, we compute the reconstruction error in local pixels between the synthesized and the real dense optical flow and then apply OHNM to remove false-positive detection results. Finally, a semantic region merging is introduced to integrate the intensities of all the individual abnormal objects into a full output frame. The proposed DR-STN has been extensively evaluated on publicly available benchmarks, including UCSD, UMN, and CUHK Avenue, demonstrating superior results over other state-of-the-art methods both in frame-level and pixel-level evaluations. The average Area Under the Curve (AUC) value of the frame-level evaluation for the three benchmarks is 96.73%. The improvement ratio of AUC in the frame level between DR-STN and state-of-the-art methods is 7.6%.

视频异常检测在当前的智能监控系统中得到了极大的关注。我们提出了深度残差时空翻译网络 (DR-STN)，这是一种具有在线硬负挖掘 (OHNM) 方法的新型无监督深度残差条件生成对抗网络 (DR-cGAN) 模型。提出的 DR-cGAN 提供了一个更广泛的网络来学习从空间到时间表示的映射，并增强来自生成器的合成图像的感知质量。在 DR-cGAN 训练期间，我们只采用正常事件的帧来产生相应的密集光流。在测试时，我们计算合成和真实密集光流之间局部像素的重建误差，然后应用 OHNM 去除假阳性检测结果。最后，引入语义区域合并以将所有单个异常对象的强度整合到完整的输出帧中。提议的 DR-STN 已在公开可用的基准上进行了广泛的评估，包括 UCSD、UMN 和 CUHK Avenue，在帧级和像素级评估方面都证明了优于其他最先进方法的结果。三个基准的帧级评估的平均曲线下面积 (AUC) 值为 96.73%。DR-STN 和 state-of-the-art 方法在帧级 AUC 的改进率为 7.6%。在帧级和像素级评估中证明了优于其他最先进方法的结果。三个基准的帧级评估的平均曲线下面积 (AUC) 值为 96.73%。DR-STN 和 state-of-the-art 方法在帧级 AUC 的改进率为 7.6%。在帧级和像素级评估中证明了优于其他最先进方法的结果。三个基准的帧级评估的平均曲线下面积 (AUC) 值为 96.73%。DR-STN 和 state-of-the-art 方法在帧级 AUC 的改进率为 7.6%。