Object Class Aware Video Anomaly Detection through Image Translation

Semi-supervised video anomaly detection (VAD) methods formulate the task of anomaly detection as detection of deviations from the learned normal patterns. Previous works in the field (reconstruction or prediction-based methods) suffer from two drawbacks: 1) They focus on low-level features, and they (especially holistic approaches) do not effectively consider the object classes. 2) Object-centric approaches neglect some of the context information (such as location). To tackle these challenges, this paper proposes a novel two-stream object-aware VAD method that learns the normal appearance and motion patterns through image translation tasks. The appearance branch translates the input image to the target semantic segmentation map produced by Mask-RCNN, and the motion branch associates each frame with its expected optical flow magnitude. Any deviation from the expected appearance or motion in the inference stage shows the degree of potential abnormality. We evaluated our proposed method on the ShanghaiTech, UCSD-Ped1, and UCSD-Ped2 datasets and the results show competitive performance compared with state-of-the-art works. Most importantly, the results show that, as significant improvements to previous methods, detections by our method are completely explainable and anomalies are localized accurately in the frames.