A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

• URL: http://arxiv.org/abs/2305.13611v1
• Date: Tue, 23 May 2023 02:20:12 GMT
• Title: A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
• Authors: Congqi Cao, Yue Lu, Peng Wang and Yanning Zhang
• Abstract summary: We propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. 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. We propose a novel model capable of detecting and anticipating anomalous events simultaneously.
• Score: 46.687762316415096
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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