Human visual attention with context-specific top-down saliency

Abstract

Human visual attention is an area of research that has a strong effect on the field of human-robot-interaction. It has various applications for human-care robots and service robots. The ability of human visual attention to acquire knowledge on informative objects through interaction with the environment is an important research issue in the field of human-computer interaction and augmented cognition. Such knowledge can be acquired by automatically detecting visually attentive regions using the fixation extracted from eye-tracking data. Therefore, the optimal fixation must be selected from human eye-tracking data to detect the region of interest. In this paper, eye movement tracking was accurately used to capture human eye movements and to characterize the location and extent of a user's interest as an input mechanism to drive the interaction system. Furthermore, both top-down and bottom-up processes of the human visual system were at work in the process of selective attention. The correlation between human eye movement information and the bottom-up saliency map was calculated and compared with the correlation that was calculated by combining the top-down and bottom-up processes. The experiment results showed that the human visual attention system cannot be constructed with the bottom-up process alone and requires the combination of the top-down and bottom-up processes together.