A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
This work addresses a gap in intelligent surveillance systems by providing a new dataset and model for detecting and anticipating scene-dependent anomalies, though it is incremental in building upon existing VAD methods.
The authors tackled the lack of datasets for scene-dependent anomalies and anomaly anticipation in video anomaly detection by introducing NWPU Campus, a comprehensive dataset with 43 scenes, 28 anomaly classes, and 16 hours of videos, and proposed a model that achieves state-of-the-art performance across multiple benchmarks.
Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://campusvad.github.io.