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.

该研究提出了一种新的两流对象感知 VAD 方法，通过图像转换任务学习正常的外观和动态模式，检测偏离学习正常模式的异常情况，并在三个数据集上表现有竞争力。