Far-Field Non-Contact Eye Tracking for Visual Attention Analysis in Runway Viewing Environments

Conventional eye trackers are difficult to deploy in fashion-runway settings because they require head stabilization, wearable devices, close-range cameras, or infrared illumination. This study develops a far-field, non-contact eye-tracking framework and prototype system for measuring target-directed visual attention in runway-viewing settings using standard cameras. The framework integrates eye-region normalization, 3D gaze estimation, individualized homography-based point-of-regard mapping onto a scene plane, adaptive filtering, DBSCAN-based outlier removal, and kernel density estimation. In Experiment 1, 16 participants completed a controlled calibration-grid benchmark with the proposed system and EyeLink 1000 Plus. Compared with the EyeLink 1000 Plus, the proposed system exhibited higher positional error ($A_{\mathrm{pos}}$: $2.50\pm 0.25°$ vs. $1.74\pm 0.55°$) and a higher value on the processed-output spatial-precision measure ($A_{\mathrm{spa}}$: $2.31\pm 0.24°$ vs. $1.02\pm 0.37°$). These results characterize end-to-end localization under the calibrated benchmark geometry and provide a controlled reference for the subsequent AOI-level evaluation. In Experiment 2, an independent sample of 16 participants completed an instructed right chest targeting task at 2 m, 4 m and 6 m. AOI-retained sample percentages were 80.4%, 97.3%, and 96.0%, respectively, and gaze-density maps concentrated within the predefined upper-body target region. These findings support the feasibility of target-directed AOI-level localization at far-field distances without chin-rest stabilization under the tested simulated runway-observation protocol.