Abstract
Video anomaly detection plays a crucial role in automatically detecting abnormal actions or events from surveillance video, which can help to protect public safety. Deep learning techniques have been extensively employed and achieved excellent anomaly detection results recently. However, previous image-reconstruction-based models did not fully exploit foreground object regions for the video anomaly detection. Some recent works applied pre-trained object detectors to provide local context in the video surveillance scenario for anomaly detection. Nevertheless, these methods require prior knowledge of object types for the anomaly which is somewhat contradictory to the problem setting of unsupervised anomaly detection. In this paper, we propose a novel framework based on learning the moving-object feature prediction based on a convolutional autoencoder architecture. We train our anomaly detector to be aware of moving-object regions in a scene without using an object detector or requiring prior knowledge of specific object classes for the anomaly. The appearance and motion features in moving objects regions provide comprehensive information of moving foreground objects for unsupervised learning of video anomaly detector. Besides, the proposed latent representation learning scheme encourages the convolutional autoencoder model to learn a more convergent latent representation for normal training data, while anomalous data exhibits quite different representations. We also propose a novel anomaly scoring method based on the feature prediction errors of moving foreground object regions and the latent representation regularity. Our experimental results demonstrate that the proposed approach achieves competitive results compared with SOTA methods on three public datasets for video anomaly detection.