A New Comprehensive Benchmark for Semi-supervised Video Anomaly
  Detection and Anticipation
Congqi Cao Yue Lu Peng Wang Yanning Zhang
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.