Summary of Collaborative Learning Of Anomalies with Privacy (clap) For Unsupervised Video Anomaly Detection: a New Baseline, by Anas Al-lahham et al.
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline
by Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new baseline for unsupervised video anomaly detection in surveillance applications, which can localize anomalous events in complex videos without any labels. The approach is designed for privacy-preserving participant-based distributed training configurations. Additionally, the paper introduces three new evaluation protocols to benchmark anomaly detection approaches on various collaboration scenarios and data availability. These protocols are used to evaluate the proposed approach as well as existing state-of-the-art methods on two large-scale datasets: UCF-Crime and XD-Violence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding strange events in surveillance videos without telling a computer what to look for. This helps keep people’s privacy safe because it doesn’t need any labeled data. The researchers created a new way to do this that can find where the weird things are happening. They also made three new ways to test how good different methods are at doing this, and they tested their own method on two big datasets. |
Keywords
* Artificial intelligence * Anomaly detection * Unsupervised
