Video Anomaly Detection via Single Frame Supervision

Junxi Chen, Liang Li, Li Su, Yunbin Tu, Zhe Xue, Qingming Huang

Keywords: Video Anomaly Detection, Inexact Supervision, Single Frame Supervision

Abstract: Video Anomaly Detection (VAD) aims to identify anomalous frames in given videos. Existing fully-supervised VAD encounters substantial annotation cost and weakly-supervised VAD suffers from the deficiency of weak labels. In this paper, we propose a more effective Single Frame supervised VAD (SF-VAD), which leverages single abnormal frame as label. We argue that single abnormal frame provides precise dual references to abnormal and normal frames, which facilitates dependable anomaly and normality modeling, and it can be obtained with negligible extra cost. Under this setting, we propose similarity-based abnormal pattern modeling, to learn inclusive abnormal patterns reliably from mined abnormal frames, guided by similarity-based abnormal probability. And we introduce Gaussian-prior normal pattern modeling to decouple normal patterns in abnormal videos, by learning normal patterns in preceding frames, guided by Gaussian-prior normal probability. In inference, we additionally design temporal decoupling and boundary refining modules to reveal discriminative abnormal characters of temporal features. Extensive experiments show our SF-VAD method outperforms state-of-the-art VAD methods and achieves an optimal performance-cost trade-off. We construct and release three SF-VAD datasets to support future research.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 6299