Aggregated mapping of driver attention from matched optical flow

Eye tracking research about driver distraction, applied to real world driving tasks, has so far demanded a massive amount of manual intervention, for the annotation of hundreds of hours of head camera videos. We present a novel methodology that enables the automated integration of arbitrary gaze localizations onto a visual object and its local surrounding in order to draw heat maps directly onto the environment. Gaze locations are tracked in video frames of the eye tracking glasses´ head camera, within the regions about the driver´s environment, using optical flow methodology. The high robustness and accuracy of the optical flow based tracking - measured with a residual mean error of ≈0.3 pixels on sequences, captured and verified in 576 individual trials - enables a fully automated estimation of the driver´s attention processes, for example in the context of roadside objects. We present results from a typical driver distraction study and visualize the performance of fully aggregated human attention behavior.