Attentional object spotting by integrating multimodal input

Abstract

An intelligent human-computer interface is expected to allow computers to work with users in a cooperative manner. To achieve this goal, computers need to be aware of user attention and provide assistance without explicit user requests. Cognitive studies of eye movements suggest that in accomplishing well-learned tasks, the performer's focus of attention is locked onto ongoing work and more than 90% of eye movements are closely related to the objects being manipulated in the tasks. In light of this, we have developed an attentional object spotting system that integrates multimodal data consisting of eye position, head position and video from the "first-person" perspective. To detect the user's focus of attention, we modeled eye gaze and head movements using a hidden Markov model (HMM) representation. For each attentional point in time, the object of user interest is automatically extracted and recognized. We report the results of experiments on finding attentional objects in the natural task of "making a peanut-butter sandwich".