Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition

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

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ethics.ai (5 August 2026), “Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition,” evidence record 16915, https://ethics.ai/record/16915 (originally published by arXiv cs.AI).

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