Abstract
Humans explore the world with their eyes, so an ideal sampling of human visual experience requires accurate gaze estimates while participants perform a wide range of activities in diverse locations. In principle, mobile eye tracking can provide this information, but in practice, many technical barriers and human factors constrain the activities, locations, and participants that can be sampled accurately. In this talk we present our progress in addressing these barriers to build the Visual Experience Database. First, we describe how the hardware design of our mobile eye tracking system balances participant comfort and data quality. Ergonomics matter, because uncomfortable equipment affects behavior and reduces the reasonable duration of recordings. Second, we describe the challenges of sampling outdoors. Bright sunlight causes squinting, casts shadows, and reduces eye video contrast, all of which reduce estimated gaze accuracy and precision. We will show how appropriate image processing at acquisition improves eye video contrast, and how DNN-based pupil detection can improve estimated pupil position. Finally, we will show how physical shift of the equipment on the head affects estimated gaze quality. We quantify the reduction in gaze precision and accuracy over time due to slippage, in terms of drift of the eye in the image frame and instantaneous jitter of the camera with respect to the eye. Addressing these limitations takes us some way towards achieving a representative sample of visual experience, but recording of long-duration, of highly dynamic activities, and in extreme lighting conditions remains challenging.