{
  "id": 16915,
  "url": "https://arxiv.org/abs/2608.05115v1",
  "title": "Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition",
  "summary": "Can computer vision help make classrooms safer? In this pilot study, we investigate privacy-aware and computationally efficient classroom incident recognition from CCTV-style observations. This setting remains underexplored, with limited benchmarks and few methods designed for the privacy, efficiency, and generalization demands of real-world deployment. We introduce a novel hybrid benchmark combining generative CCTV-style videos with real-world classroom pose data, and propose a lightweight, but",
  "authors": "Paritosh Parmar, Landy Lan, Hong Yang, Chen Yi, Chiat Pin Tay",
  "category": "research",
  "topics": "privacy-surveillance,children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T17:46:28.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16915",
  "original_url": "https://arxiv.org/abs/2608.05115v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}