Privacy-Preserving Video Anomaly Detection: A Survey
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View abstract on PubMed
Summary
This summary is machine-generated.This review systematically examines privacy-preserving video anomaly detection (P2VAD), addressing fragmented research and privacy concerns in surveillance. It offers a taxonomy, analyzes methods, and discusses future directions for AI development and P2VAD deployment.
Area Of Science
- Artificial Intelligence
- Computer Vision
- Cybersecurity
Background
- Video anomaly detection (VAD) is crucial for public safety but raises privacy concerns due to personally identifiable information in surveillance footage.
- Existing VAD systems often lack transparency, limiting real-world applications and public trust.
- Privacy-preserving VAD (P2VAD) has emerged as a research hotspot, yet current studies are fragmented and often overlook privacy leakage and appearance bias.
Purpose Of The Study
- To systematically review the progress of privacy-preserving video anomaly detection (P2VAD).
- To define the scope and provide an intuitive taxonomy for P2VAD research.
- To identify challenges and opportunities for future P2VAD development and deployment.
Main Methods
- Comprehensive literature review of P2VAD research.
- Development of a novel taxonomy for classifying P2VAD approaches.
- Analysis of assumptions, learning frameworks, and optimization objectives of various P2VAD methods.
- Evaluation of strengths, weaknesses, and potential correlations among different approaches.
Main Results
- This article presents the first systematic review of P2VAD, offering a structured overview of the field.
- A clear taxonomy is provided, categorizing P2VAD methods based on their approaches to privacy preservation.
- Strengths, weaknesses, and interconnections of various P2VAD techniques are analyzed.
Conclusions
- The review highlights the fragmented nature of current P2VAD research and identifies gaps, particularly concerning RGB sequence-based methods.
- Open-access resources, including datasets and code, are provided to facilitate further research.
- Key challenges and future opportunities are discussed to guide advancements in AI development and P2VAD deployment.
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