Affiliations: [a] School of Automation, Beijing Institute of Technology, Beijing, China | [b] Mechanical and Industrial Engineering Department, Northeastern University, Boston, MA, USA | [c] School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, China | [d] School of Life, Beijing Institute of Technology, Beijing, China
Correspondence:
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Corresponding author: Xiangzhou Wang, School of Automation, Beijing Institute of Technology, Detailed Permanent address: 703 Room, 6 Building, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China. Tel.: +86 13910845860; Fax: +86 010 68918820; E-mail:wangxiangzhoubit@gmail.com
Abstract: 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.
