User interest profiling using tracking-free coarse gaze estimation
Understanding where people attention focuses is a challenging and extremely valuable task that can be solved using computer vision technologies. In this paper we address this problem on surveillance-like scenarios, where head and body imagery are usually low resolution. We propose a method to profile the attention of people moving in a known space. We exploit coarse gaze estimation and a novel model based on optical flow to improve attention prediction without the need of a tracker. Removing the tracker dependency makes the method applicable also on highly crowded scenarios. The proposed method is able to obtain comparable performance with respect to state of the art solutions in terms of Mean Average Angular Error (MAAE) on the TownCentre dataset. We also test our approach on the publicly available MuseumVisitors dataset showing an improvement both in terms of MAAE and in terms of accuracy in the estimation of visitors' profile.