Tracking driver signage observation using local feature matching and optical flow

We investigate a method for identifying objects observed by drivers. Here we focus on roadside signage as an example, and track the driver´s observation of signage while driving. A gaze tracking system and a forward-directed video camera are used to determine the driver´s region of interest (ROI). The driver´s observation of signage is detected by tracking the driver´s ROI using optical flow, and by matching the driver´s ROI with template images of signboards in a signage database using local feature matching. Driver and signage location information are used to limit candidate signboards for reducing computational cost for image matching. We conduct an experiment to evaluate our method and achieve a 66.2% detection rate of drivers´ signboard observation with a false positive rate of 6.6%.