Eye gaze driven spatio-temporal action localization

Abstract

The disclosure provides an approach for detecting and localizing action in video. In one embodiment, an action detection application receives training video sequences and associated eye gaze fixation data collected from a sample of human viewers. Using the training video sequences and eye gaze data, the action detection application learns a model which includes a latent regions potential term that measures the compatibility of latent spatio-temporal regions with the model, as well as a context potential term that accounts for contextual information that is not directly produced by the appearance and motion of the actor. The action detection application may train this model in, e.g., the latent structural SVM framework by minimizing a cost function which encodes the cost of an incorrect action label prediction and a mislocalization of the eye gaze. During training and thereafter, inferences using the model may be made using an efficient dynamic programming algorithm.