Papers › VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
Sunghyun Ahn, Youngwan Jo, Kijung Lee, Sanghyun Park
Video anomaly detection (VAD) is a crucial task in video analysis and surveillance within computer vision. Currently, VAD is gaining attention with memory techniques that store the features of normal frames. The stored features are utilized for frame reconstruction, identifying an abnormality when a significant difference exists between the reconstructed and input frames. However, this approach faces several challenges due to the simultaneous optimization required for both the memory and encoder-decoder model. These challenges include increased optimization difficulty, complexity of implementation, and performance variability depending on the memory size. To address these challenges,we propose an effective memory method for VAD, called VideoPatchCore. Inspired by PatchCore, our approach introduces a structure that prioritizes memory optimization and configures three types of memory tailored to the characteristics of video data. This method effectively addresses the limitations of existing memory-based methods, achieving good performance comparable to state-of-the-art methods. Furthermore, our method requires no training and is straightforward to implement, making VAD tasks more accessible. Our code is available online at github.com/SkiddieAhn/Paper-VideoPatchCore.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | CUHK Avenue | VideoPatchCore | AUC | 92.8% | #9 of 35 | Archive leaderboard | report |
| Anomaly Detection | IITB Corridor | VideoPatchCore | AUC | 76.4% | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech | VideoPatchCore | AUC | 85.1% | #6 of 31 | Archive leaderboard | report |
| Video Anomaly Detection | CUHK Avenue | VideoPatchCore | AUC | 92.8% | #1 of 7 | Archive leaderboard | report |
| Video Anomaly Detection | IITB Corridor | VideoPatchCore | AUC | 76.4% | #1 of 1 | Archive leaderboard | report |
| Video Anomaly Detection | ShanghaiTech | VideoPatchCore | AUC | 85.1% | #3 of 7 | 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.
Methods
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