Abstract: In-cabin monitoring systems use cameras for fundamental detection of the driver and occupant activity. Within the camera, the image sensor is a key component for converting the incoming light into a signal to be used by downstream machine vision algorithms. In-cabin cameras use image sensors designed to be optimized to a near infrared (NIR) signal, as this segment of the electromagnetic spectrum can illuminate the driver and occupant without interference of the primary driving task inside the vehicle. Quantifying the camera’s NIR performance, unlike traditional cameras that use tools designed for the visible spectrum, requires image quality tools optimized for NIR. This talk will describe such tools that can be used to measure image quality factors including sharpness, noise, distortion, flare, and tonal properties such as dynamic range. We will also be describing a novel approach to measure information capacity and related KPIs (key performance indicators). These measurements can then be used to optimize the camera systems to increase performance of the driver and occupant monitoring systems (DMS and OMS).
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