Surveillance Video Anomaly Detection with Feature Enhancement and Consistency Frame Prediction

Abstract

Surveillance video anomaly detection is a challenging problem because of the diversity of abnormal events. The current prediction-based methods outperform reconstruction-based methods. But the former has the following issues: 1) Using optical flow to represent motion will affect real-time detection. 2) Distinguishing abnormal events only by local relationships will lead to ambiguity. 3) Semantic information and spatiotemporal constraint are not fully utilized. To address these problems, we propose FECP-Net: a network with feature enhancement and consistency frame prediction for surveillance video anomaly detection. We use the RGB difference between consecutive frames rather than optical flow to realize real-time detection. Meanwhile, we design a feature enhancement module to enrich semantics and global context information in features. In addition, we add spatiotemporal consistency constraint and consistency loss to strengthen consistency predictions. Extensive experiments on standard benchmarks demonstrate the effectiveness of our method.