Summary of Low-latency Video Anonymization For Crowd Anomaly Detection: Privacy Vs. Performance, by Mulugeta Weldezgina Asres and Lei Jiao and Christian Walter Omlin
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance
by Mulugeta Weldezgina Asres, Lei Jiao, Christian Walter Omlin
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Recent advancements in artificial intelligence have made surveillance cameras a promising tool for monitoring applications. However, concerns about privacy and model bias have hindered their adoption in public spaces. De-identification approaches have been proposed to anonymize data, but most rely on computationally demanding deep learning models, making real-time edge deployment challenging. This study revisits conventional anonymization solutions for video anomaly detection (VAD) applications, proposing a novel lightweight adaptive anonymization technique (LA3D) that dynamically adjusts privacy protection. LA3D is evaluated on publicly available data sets, demonstrating substantial improvement in privacy anonymization capability without degrading VAD efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence can help with monitoring and surveillance, but there are concerns about people’s privacy being protected. Right now, it’s hard to use cameras for this purpose because they might not be designed well enough to keep personal information private. This study looks at ways to make sure data is anonymous while still working well for detecting unusual events in video footage. They propose a new approach that can quickly and effectively anonymize data without sacrificing its ability to detect anomalies. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning
