Summary of Privacy-preserving Video Anomaly Detection: a Survey, by Jing Liu et al.
Privacy-Preserving Video Anomaly Detection: A Survey
by Jing Liu, Yang Liu, Xiaoguang Zhu
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); 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 A novel Video Anomaly Detection (VAD) approach aims to automatically analyze spatiotemporal patterns in surveillance videos to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems often capture personally identifiable information, raising privacy and ethics concerns, limiting the real-world application of VAD. Researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in AI. Current research in P2VAD is fragmented, mostly focusing on RGB sequences, overlooking privacy leakage and appearance bias considerations. This paper systematically reviews the progress of P2VAD for the first time, defining its scope and providing an intuitive taxonomy. Various approaches are outlined, analyzing their strengths, weaknesses, and potential correlations. Open access to research resources such as benchmark datasets and available code is provided. Key challenges and future opportunities from AI development and P2VAD deployment are discussed, aiming to guide future work in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video Anomaly Detection (VAD) tries to detect unusual events in videos taken from open spaces. But these videos often show personal information, making people worry about privacy and fairness. To make VAD better, researchers have been studying how to keep private information safe while still finding anomalies. Right now, research on this topic is scattered and mostly focuses on color-based video analysis, ignoring other important issues. This paper brings all the current work together for the first time, making it easy to understand what’s happening in this area of AI. |
Keywords
» Artificial intelligence » Anomaly detection » Spatiotemporal
