Scanpath mining of eye movement trajectories for visual attention analysis

Abstract

Eye movement reflects the shift of overt visual attention. Eye movement trajectories from a group of observers can be expressed by a representative scanpath. The representative scan-path can work as a baseline for studies on scanpath prediction as well as provide useful knowledge about group behavior in psychological studies. In this paper, we propose a new framework to summarize a representative scanpath from individual scanpaths, taking into account the spatial distribution of scan-paths rather than simply treating them as strings of characters. It consists of three steps: extract areas of interest (AOI), remove outliers and summarize scanpaths. In the last step, we develop an algorithm termed Candidate-constrained DTW Barycenter Algorithm (CDBA) by imposing 3 constraints: (1) the components of the representative scanpath must be chosen from candidates (extracted AOIs); (2) the occurrence count of each AOI in the representative scanpath cannot exceed its maximum occurrence count in individual scanpaths; (3) any two contiguous AOIs in the representative scanpath must be contiguous in at least one individual scanpath. The experiments demonstrate that the proposed method outperforms other state-of-the-art scanpath mining methods.