Fixation Prediction and Visual Priority Maps for Biped Locomotion

This paper presents an analysis of the low-level features and key spatial points used by humans during locomotion over diverse types of terrain. Although, a number of methods for creating saliency maps and task-dependent approaches have been proposed to estimate the areas of an image that attract human attention, none of these can straightforwardly be applied to sequences captured during locomotion, which contain dynamic content derived from a moving viewpoint. We used a novel learning-based method for creating a visual priority map informed by human eye tracking data. Our proposed priority map is created based on two fixation types: first exploiting the observation that humans search for safe foot placement and second that they observe the edges of a path as a guide to safe traversal of the terrain. Texture features and the difference between them, observed at the region around an eye position, are employed within a support vector machine to create a visual priority map for biped locomotion. The results show that our proposed method outperforms the state-of-the-art, particularly for more complex terrains, where achieving smooth locomotion needs more attention on the traversing path.