In-cabin cameras are becoming increasingly important for both enhancing user experience and enabling advanced driver and passenger monitoring systems. However, optimizing camera performance for both human and machine vision presents distinct challenges, particularly when it comes to testing and evaluation. This tutorial will focus on the key performance indicators (KPIs) and testing methodologies used to assess image quality for both human and machine vision use cases. For human vision, KPIs such as color accuracy, sharpness, and noise are critical for applications like video conferencing and selfies. For machine vision, metrics like contrast, dynamic range, and signal-to-noise ratio are essential for reliable detection of head position, drowsiness, and eye gaze under varying lighting conditions. Led by an expert in image quality analysis and camera characterization, this session will provide attendees with a structured approach to defining, measuring, and balancing KPIs to optimize in-cabin camera systems for both human and machine vision requirements.
