A spatial-temporal trajectory clustering algorithm for eye fixations identification

Eye movements mainly consist of fixations and saccades. The identification of eye fixations plays an important role in the process of eye-movement data research. At present, there is no standard method for identifying eye fixations. In this paper, eye movements are regarded as spatial-temporal trajectories. Hence, we present a spatial-temporal trajectory clustering algorithm for eye fixations identification. The main idea of the algorithm is based on Density-Based Spatial Clustering Algorithm with Noise (DBSCAN), which is commonly used in spatial clustering data. In order to apply DBSCAN to our spatial-temporal clustering data, we modified its original concept and algorithm. In addition, the optimum dispersion threshold (Eps) is derived automatically from the data sets with the aid of the 'gap statistic' theory. Using the confusion matrix measurement method, we compared the classification results obtained by our algorithm with four other expert algorithms for eye fixations identification show the proposed algorithm demonstrated an equal or better performance. Also, the robustness of our algorithm to additional noise in Points of Gaze (PoGs) data and changes in sampling rate has been verified.