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An Attribute-based Method for Video Anomaly Detection
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abstract
Video anomaly detection (VAD) identifies suspicious events in videos, which is critical for crime prevention and homeland security. In this paper, we propose a simple but highly effective VAD method that relies on attribute-based representations. The base version of our method represents every object by its velocity and pose, and computes anomaly scores by density estimation. Surprisingly, this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the most commonly used VAD dataset. Combining our attribute-based representations with an off-the-shelf, pretrained deep representation yields state-of-the-art performance with a $99.1\%, 93.7\%$, and $85.9\%$ AUROC on Ped2, Avenue, and ShanghaiTech, respectively.
Forward citations
Cited by 2 Pith papers
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Autoregressive Denoising Score Matching is a Good Video Anomaly Detector
An autoregressive denoising score matching method, combining scene, motion, and appearance cues, achieves state-of-the-art video anomaly detection on Avenue, ShanghaiTech, and NWPU Campus.
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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM
The paper organizes VAD methods into a five-dimension framework spanning task objective, modality, input, architecture, and optimization, with emphasis on MLLM/LLM-era work.
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