Papers › An Attribute-based Method for Video Anomaly Detection
An Attribute-based Method for Video Anomaly Detection
Tal Reiss, Yedid Hoshen
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.
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Code
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Code Syntology ran Syntology
3 samples harvested; 2 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abnormal Event Detection In Video | UCSD Ped2 | AI-VAD | AUC | 99.1 | #1 of 4 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | AI-VAD | AUC | 93.7% | #4 of 35 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech | AI-VAD | AUC | 85.94% | #3 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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