arXiv 2610.148462026-10-14cs.CVeess.SP

面向隐私保护的热红外人体感知：从数据集到部署

Towards Privacy-Preserving Thermal Human Perception from Dataset to Deployment

Massachusetts Institute of Technology（麻省理工学院）；Singapore-MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工研究与技术联盟中心（SMART））；Chongqing General Hospital（重庆总医院）；Chongqing University（重庆大学）；Hong Kong Xsmart Century Technology Co., Ltd.（香港智享世纪科技有限公司）；National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）

Kailai Sun, Yue Cui, Xinwei Wang, Zijie Gao, Baoshen Guo, Zhiqing Hong, Irfan Qaisar, Xu Yang, Yueting Li, Dian Fan, Qianchuan Zhao

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AI总结：

本文构建大规模Indoor RGB-T数据集，建立热红外感知基准并实现图像转换，将模型部署到商用睡眠监测系统，验证了其实际可行性，为隐私保护的室内人体感知提供支撑。

摘要：

室内人体感知是智能家居与医疗保健的基础，但RGB相机在私人室内环境中存在隐私问题，而低分辨率热红外相机提供的空间与语义信息有限，大规模室内热红外数据集及实际部署仍较匮乏。本文提出Indoor RGB-T，这是一个用于室内人体感知的大规模配对RGB-热红外数据集，涵盖20个室内房间，包含90576帧80×62热红外图像、87016个时间匹配的RGB-热红外图像对，以及115557个标注的人体边界框。我们建立了基准：针对鲁棒热红外居住者状态检测的平均精度（mAP）为0.75，针对未见过房间的居住者估计准确率为0.847；进一步评估RGB到热红外、热红外到RGB的图像转换，温度平均绝对误差（MAE）为0.654℃，决定系数（R²）为0.923。最后，我们将热红外感知模型部署到商用睡眠监测系统中，对100名参与者的实地测试及约1000台设备的部署验证了其实际可行性。我们还指出了推动未来研究方向的实际挑战，包括环境热干扰、感知范围及多人遮挡，代码、训练模型及部分数据集可在指定URL获取。

英文摘要：

Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, while low-resolution thermal cameras provide limited spatial and semantic information. Large-scale indoor thermal datasets and real-world deployments remain limited. Here, we introduce Indoor RGB-T, a large-scale paired RGB--thermal dataset for indoor human perception. The dataset covers 20 indoor rooms and contains 90,576 80x62 thermal frames, 87,016 temporally matched RGB--thermal pairs, and 115,557 annotated human bounding boxes. We establish benchmarks for robust thermal occupant-state detection with 0.75 mAP and occupancy estimation with an accuracy of 0.847 in unseen rooms. We further evaluate RGB-to-thermal and thermal-to-RGB translation, achieving a temperature MAE of 0.654 C and an R2 of 0.923. Finally, we deploy the thermal perception model on a commercial sleep-monitoring system in the real world. Field testing with 100 participants and deployment of approximately 1,000 devices further demonstrate its practical feasibility. We identify practical challenges that motivate future research directions, including environmental thermal interference, sensing range and multi-person occlusion. The code, trained models and partial dataset are available at https://github.com/kailaisun/RGBT and https://huggingface.co/skl24/RGBT.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。