Spatiotemporal consistency-enhanced network for video anomaly detection

Highlights：

• A well-designed 3D-2D U-shape structure is introduced to extract motion and appearance fusion features from the input video clip, which focuses on extracting spatiotemporal high-level features and generating a rational future frame. Moreover, a resampling module is used to enlarge the score gaps between normal and abnormal contents for the video anomaly detection task.

• Extensive experiments on three datasets demonstrate the potential of our model with competitive performance compared with state-of-the-art approaches. We also provide discussions for these datasets, which could be useful for future works.

摘要

•We propose a spatiotemporal consistency-enhanced network (STCEN) to highlight the disturbances of abnormal data from both spatial and temporal aspects. A 3D CNN-based discriminator is designed to measure the spatiotemporal consistency between the generated future frame and its former frames.•A well-designed 3D-2D U-shape structure is introduced to extract motion and appearance fusion features from the input video clip, which focuses on extracting spatiotemporal high-level features and generating a rational future frame. Moreover, a resampling module is used to enlarge the score gaps between normal and abnormal contents for the video anomaly detection task.•Extensive experiments on three datasets demonstrate the potential of our model with competitive performance compared with state-of-the-art approaches. We also provide discussions for these datasets, which could be useful for future works.