Under the sponsorship of the National Surface Transportation Safety Center for Excellence (NSTSCE), a research team at the Virginia Tech Transportation Institute (VTTI) developed a color camera system that can collect naturalistic video data with accurate color rendering. Photometric devices can accurately measure color but cannot record the video data necessary for understanding visibility in dynamic environments like nighttime driving. Video recorders can take video data but are inaccurate with respect to color measurement. To measure color and its effects on visibility in naturalistic settings, a color camera system was developed that can record video data with color rendering similar to what humans perceive. This system includes a calibrated color camera and image analysis software. The camera system was selected and calibrated in different lighting scenarios using a standard color chart. Custom MATLAB programs were used for this calibration. These calibration files were compared for color-rendering accuracy, and the best file, based on calibration in daylight, was selected for further analysis. Researchers then used the color camera system, calibrated with the daylight file, to collect data in a variety of naturalistic settings. The color space coordinates from the color camera’s images were compared with those taken with a color meter and a digital photometer. When the camera was calibrated to daylight, it produced the most-accurate images, even when taking images in artificial lighting. Shorter exposure times produced darker images but more-accurate color space coordinates. After calibration and exposure adjustment, the color camera’s chromaticity coordinates (x, y) had about 10% error with respect to the color meter. The color camera’s luminance value (Y) had less than 5% error with respect to the color meter. The calibration file produced can be used with multiple cameras. A new image analysis method was developed. It and its accompanying custom MATLAB programs allow researchers to select portions of an image and analyze their three-dimensional color space coordinates. This capability will be useful in future work; for example, comparing photometric equipment, and analyzing naturalistic video data.