出版社：The Japanese Society for Artificial Intelligence
摘要：Researchers are able to estimate what subjects attend to by using eye tracking systems. Existing approaches for analyzing eye movements are very useful to estimate attention to still objects. But they are inadequate to estimate attention to moving objects, although paying attention to moving objects is usual human behavior. Thus, we propose a novel approach and algorithm to estimate attention to moving objects more precisely. Our approach is to extract "eye tracking movements". We phrase both saccadic eye movements and smooth pursuit eye movements as "eye tracking movements". Because previous works reveal that humans often track a moving object by the eye movements when they pay attention to the object, our approach seems to be appropriate. Our algorithm used for extracting eye tracking movements is called Path Stitching (PS) algorithm. The PS algorithm is a kind of Longest Common Subsequence (LCS) discovery algorithms using Dynamic Time Warping (DTW) as a similarity measurement procedure. Eye tracking movements can be regarded as a situation that a subsequence of eye movements' trajectory is similar to a subsequence of the target's trajectory with a small or no time-difference. Because humans track a moving object by the eye movements with locally fluctuated time difference, we think the use of DTW is very appropriate. In addition, to make outputs more comprehensible, we employ the LCS discovery approach for the PS algorithm. The experimental results show that the PS algorithm is more able to extract eye tracking movements than the fixation extracting approach. The fixation extracting approach is the most popular and the strongest approach to analyze eye movements. Therefore, we conclude that our novel approach will advance the analysis of human behavior by the eye movements and human-computer interaction systems they utilize user's eye movements.
关键词：eye movement ; dynamic time warping ; longest common subsequence ; time series ; visual attention
