Video viewer state estimation using gaze tracking and video content analysis
Jae-Woo Kim, Jong‐Ok Kim · 2013
In this paper, we propose a novel viewer state model based on gaze tracking and video content analysis. There are two primary contributions in this paper. We first improve gaze state classification significantly by combining video content analysis. Then, based on the estimated gaze state, we propose a novel viewer state model indicating both viewer's interest and existence of viewer's ROIs. Experiments were conducted to verify the performance of the proposed gaze state classifier and viewer state model. The experimental results show that the use of video content analysis in gaze state classification considerably improves the classification results and consequently, the viewer state model correctly estimates the interest state of video viewers.