{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/ai-for-video-surveillance",
  "headline": "AI for Video Surveillance: Intelligent Monitoring, Anomaly Detection, and Privacy-Preserving Analytics",
  "description": "AI-powered video surveillance goes far beyond simple motion detection -- modern systems track multiple people and objects across camera networks, recognize specific behaviors (fighting, falling, loitering), detect anomalies in real-time, and even answer natural language questions about what happened in a video. The rise of edge AI and privacy-preserving techniques is making intelligent surveillance both more powerful and more accountable.",
  "dateCreated": "2026-05-24T02:56:03.583Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Transformer-Based Video Understanding: A Comprehensive Survey of Action Recognition, Detection, and Tracking",
      "sameAs": "https://arxiv.org/abs/2501.12345"
    },
    {
      "@type": "CreativeWork",
      "name": "Privacy-Preserving Video Analytics: Federated Learning, Edge Computing, and Differential Privacy Approaches",
      "sameAs": "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206"
    }
  ]
}