Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence

• URL: http://arxiv.org/abs/2601.17050v1
• Date: Wed, 21 Jan 2026 09:11:26 GMT
• Title: Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence
• Authors: Hongjun An, Yiliang Song, Jiawei Shao, Zhe Sun, Xuelong Li,
• Abstract summary: We propose the Single-Pixel Vision-Language Model (SP-VLM), a novel framework that reimagines secure environmental monitoring.<n>It achieves intrinsic privacy-by-design by capturing human dynamics through inherently low-dimensional single-pixel modalities.<n>We show that SP-VLM can nonetheless extract meaningful behavioral semantics, enabling robust anomaly detection, people counting, and activity understanding.
• Score: 55.512671026669516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adverse social interactions, such as bullying, harassment, and other illicit activities, pose significant threats to individual well-being and public safety, leaving profound impacts on physical and mental health. However, these critical events frequently occur in privacy-sensitive environments like restrooms, and changing rooms, where conventional surveillance is prohibited or severely restricted by stringent privacy regulations and ethical concerns. Here, we propose the Single-Pixel Vision-Language Model (SP-VLM), a novel framework that reimagines secure environmental monitoring. It achieves intrinsic privacy-by-design by capturing human dynamics through inherently low-dimensional single-pixel modalities and inferring complex behavioral patterns via seamless vision-language integration. Building on this framework, we demonstrate that single-pixel sensing intrinsically suppresses identity recoverability, rendering state-of-the-art face recognition systems ineffective below a critical sampling rate. We further show that SP-VLM can nonetheless extract meaningful behavioral semantics, enabling robust anomaly detection, people counting, and activity understanding from severely degraded single-pixel observations. Combining these findings, we identify a practical sampling-rate regime in which behavioral intelligence emerges while personal identity remains strongly protected. Together, these results point to a human-rights-aligned pathway for safety monitoring that can support timely intervention without normalizing intrusive surveillance in privacy-sensitive spaces.

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