Driver fixation region division–oriented clustering method based on the density-based spatial clustering of applications with noise and the mathematical morphology clustering

Abstract

A clustering method that combined density-based spatial clustering of applications with noise with mathematical morphology clustering is proposed to adapt to the features of driver’s fixation such as points’ dispersion and fixation regions’ irregularity and solve the problems of conventional density-based spatial clustering of applications with noise method’s large influence by parameters and mathematical morphology clustering’s needs of much manual intervention. Drivers’ fixation data were collected by Smart Eye Pro 5.7 eye tracking system, and the data were processed and clustered using conventional clustering methods and density-based spatial clustering of applications with noise–mathematical morphology clustering method. The results show that the method proposed in this article takes into account the advantages of density-based spatial clustering of applications with noise and mathematical morphology clustering to cluster irregular regions and makes up for defects of conventional clustering methods. It is verified that density-based spatial clustering of applications with noise–mathematical morphology clustering method is better than the conventional hierarchical clustering method and density-based spatial clustering of applications with noise method in driver’s fixation points clustering and can improve the quality of driver’s fixation region division.