Towards gaze-based video annotation

This paper presents our efforts towards a framework for video annotation using gaze. In computer vision, video annotation (VA) is an essential step in providing a ground truth for the evaluation of object detection and tracking techniques. VA is a demanding element in the development of video processing algorithms, where each object of interest should be manually labelled. Although the community has handled VA for a long time, the size of new data sets and the complexity of the new tasks pushes us to revisit it. A barrier towards automated video annotation is the recognition of the object of interest and tracking it over image sequences. To tackle this problem, we employ the concept of visual attention for enhancing video annotation. In an image, human attention naturally grasps interesting areas that provide valuable information for extracting the objects of interest, which can be exploited to annotate videos. Under task-based gaze recording, we utilize an observer's gaze to filter seed object detector responses in a video sequence. The filtered boxes are then passed to an appearance-based tracking algorithm. We evaluate the gaze usefulness by comparing the algorithm with gaze and without it. We show that eye gaze is an influential cue for enhancing the automated video annotation, improving the annotation significantly.