{"id":12341,"date":"2026-03-26T09:22:17","date_gmt":"2026-03-26T08:22:17","guid":{"rendered":"https:\/\/www.terabee.com\/?p=12341"},"modified":"2026-03-26T09:22:20","modified_gmt":"2026-03-26T08:22:20","slug":"the-privacy-paradox-in-people-counting-why-rgb-based-ai-and-stereovision-may-fall-short-of-modern-compliance-standards","status":"publish","type":"post","link":"https:\/\/www.terabee.com\/fr\/the-privacy-paradox-in-people-counting-why-rgb-based-ai-and-stereovision-may-fall-short-of-modern-compliance-standards\/","title":{"rendered":"The Privacy Paradox in People Counting: Why RGB-Based AI and Stereovision May Fall Short of Modern Compliance Standards"},"content":{"rendered":"\t\t
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As the demand for occupancy data and foot-fall analytics grows, a technical divide has emerged in the IoT sector. On one hand, many solution providers utilize High-Definition (HD) RGB cameras, often in a dual-lens Stereovision<\/strong> configuration, paired with Artificial Intelligence (AI) to achieve high accuracy. On the other hand, a \u00ab\u00a0Privacy-by-Design\u00a0\u00bb approach utilizes non-optical sensors and deterministic algorithms.

This paper explores how different sensing technologies approach GDPR compliance, and highlights potential residual risks associated with optical systems, particularly in the context of the evolving EU AI Act and broader cybersecurity considerations.<\/p>\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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1. The RGB and Stereovision Dilemma: Data Over-Collection<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The General Data Protection Regulation (GDPR) is built on the principle of Data Minimization<\/strong>: personal data should be adequate, relevant, and limited to what is necessary for the purpose.

The Optical Approach (RGB & Stereovision):<\/strong>
Most modern people-counting sensors are essentially sophisticated RGB cameras. Even \u00ab\u00a0Stereovision\u00a0\u00bb sensors, which use two lenses to calculate depth, typically rely on standard optical sensors to create those images. They capture rich, high-resolution visual data (faces, clothing, gender, and gait). While the AI \u00ab\u00a0filters\u00a0\u00bb this into numerical counts at the edge, the input remains highly sensitive personal data.

The Privacy-by-Design Approach:<\/strong> By using non-RGB sensors, such as Time-of-Flight (ToF), Thermal, or Radar, the device never \u00ab\u00a0sees\u00a0\u00bb a human face or identifiable feature. The raw data consists of depth maps or heat signatures that are inherently meaningless to a human observer or a hacker. This is true data minimization: you cannot lose or leak what you never captured in the first place. <\/p>\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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2. The EU AI Act: A New Regulatory Hurdle<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The recently enacted EU AI Act<\/strong><\/a><\/span> introduces a risk-based framework for AI systems. Solutions that rely on computer vision and AI for certain types of biometric or behavioral analysis may face increased regulatory scrutiny depending on their use case. <\/p>