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ReSPEcT: Privacy Respecting Thermal Based Specific Person Recognition

机译：尊重：尊重基于热基的特定人员认可的隐私

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Video analytic techniques have been used to extract high level information from video streams. The technique leverages advances on machine learning to summarize complex image data into simple alert-signal to attract the attention of human operators. For example, in a station for the underground video analytic can help the operator to focus on an event from a specific camera rather than leaving this only to the human eye. A concern of such techniques is privacy as they expose people identity and enable profiling of personal habits and orientations. This work introduces ReSPEcT (Privacy Respecting theRmal basEd Specific Person rECogniTion), a privacy preserving video analytic system based on thermal video streams. ReSPEcT is able to identify a specific-human in thermal video streams from low-cost, low resolution cameras. The system leverages recent advances in machine learning (CNNs) and a plethora of pre-processing mechanisms, such as image automatic labeling, image segmentation, and image augmentation to reduce the stream background noise, improve resilience, strengthen human-body classification, and finally enable a specific human-target identification. ReSPEcT's automatic labeling tool significantly reduces time thus automatically performing labeling using a model that can be retrained by an interactive web application. The experimental evaluation shows that overall ReSPEcT achieve 96.83% accuracy in identifying a specific person. Furthermore, is important to notice that while ReSPEcT can identify a specific human, the tool is not aware of the real-identity as it operates only on thermal images. ReSPEcT paves the way to use video analytic in a variety of privacy-protected scenarios, such as confidential meetings, sensitive spaces, or even public toilets.
机译：视频分析技术已被用于从视频流中提取高级信息。该技术利用机器学习的进步将复杂的图像数据总结为简单的警报信号，以吸引人类运营商的注意。例如，在地下视频分析的站中可以帮助操作员专注于特定相机的事件，而不是将此留给人眼。这种技术的担忧是隐私，因为他们暴露了人们的身份并实现了个人习惯和方向的分析。这项工作介绍了尊重（尊重基于热基的特定人员识别），这是一种基于热视频流的隐私保存视频分析系统。尊重能够从低成本低分辨率相机识别热量视频流中的特定人。该系统利用机器学习（CNNS）的最近进步和一种预处理机构，例如图像自动标记，图像分割和图像增强，以减少流背景噪声，改善弹性，加强人体分类，并最终启用特定的人体目标识别。尊重的自动标签工具显着减少了使用可以由交互式Web应用程序迁移的模型自动执行标记的时间。实验评估表明，整体尊重达到了识别特定人员的准确性为96.83％。此外，重要的是要注意虽然尊重可以识别特定的人类，但该工具不知道它仅在热图像上运行的实际身份。尊重铺平了在各种保护方案中使用视频分析的方式，例如机密会议，敏感空间，甚至公共厕所。

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