Revisiting visual attention identification based on eye tracking data analytics
Visual attention identification is crucial to human visual perception analysis and relevant applications. In this paper, we propose a comprehensive visual attention identification algorithm consisting of clustering and center identification. In the clustering process, a spatial-temporal affinity propagation method for accurate fixation clustering is proposed. For the identified clusters, the random walk based method is utilized to extract the center of each cluster, which presents the essential part of an area of interest (AOI). The proposed approach addresses the problem of fixation overlapping in eye movement analytics. Compared with the state-of-the-arts methods, the proposed method shows superior performance for different eye tracking experiments.