Human Interaction with mobile devices has recently been established as application field in eye tracking research. Current technologies for gaze recovery on mobile displays cannot enable fully natural interaction with the mobile device: users are conditioned to interact with tightly mounted displays or distracted by markers in their view. We propose a novel approach that captures point-of-regards (PORs) with eye tracking glasses (ETG) and then uses computer vision methodology for the robust localization of the smartphone in the head camera video. We present an integrated software package, i.e., the Smartphone Eye Tracking Toolbox (SMET) that enables accurate gaze recovery on mobile displays with heat mapping of recent attention. We report the performance of the computer vision approach and demonstrate it with various natural interaction scenarios using the SMET Toolbox, enable ROI settings on the mobile display and show results from eye movement analysis, such as, ROI dwell time and statistics on eye gaze event (saccades, fixations).
We introduce the discipline of Acoustic Geo-Sensing (AGS) that deals with the connection of acoustics and geoposition, i. e., 'local audio' -focussing on spatial rather than on temporal aspects. We motivate this field of research, and give an example by automatic determination of a cyclist's route between determined start and endpoints, the direction she advances on this route, and the progress made from cell-phone audio. The Graz Cell-phone Cycle Corpus of 16 hours audio is introduced to this end. A standardised acoustic feature set ensures reproducibility throughout extensive experimentation aiming to reveal maximal spatiotemporal resolution. In the result, principle feasibility is shown by unsupervised clustering and all presented tasks can be solved at high accuracies and correlation within Random Forest classification and Additive Regression.
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