Real-Time Weakly Supervised Video Anomaly Detection

Abstract

Weakly supervised video anomaly detection is an important problem in many real-world applications where during training there are some anomalous videos, in addition to nominal videos, without labelled frames to indicate when the anomaly happens. State-of-the-art methods in this domain typically focus on offline anomaly detection without any concern for real-time detection. Most of these methods rely on ad hoc feature aggregation techniques and the use of metric learning losses, which limit the ability of the models to detect anomalies in real-time. In line with the premise of deep neural networks, there also has been a growing interest in developing end-to-end approaches that can automatically learn effective features directly from the raw data. We propose the first real-time and end-to-end trained algorithm for weakly supervised video anomaly detection. Our training procedure builds upon recent action recognition literature and trains a large video model to learn visual features. This is in contrast to existing approaches which largely depend on pre-trained feature extractors. The proposed method significantly improves the anomaly detection speed and AUC performance compared to the existing methods. Specifically, on the UCF-Crime dataset, our method achieves 86.94% AUC with a decision period of 6.4 seconds while the competing methods achieve at most 85.92% AUC with a decision period of 273 seconds.