A survey of activity recognition in egocentric lifelogging datasets
We present an online fully unsupervised approach for automatically extracting video guides of how objects are used from wearable gaze trackers worn by multiple users. Given egocentric video and eye gaze from multiple users performing tasks, the system discovers task-relevant objects and automatically extracts guidance videos on how these objects have been used. In the assistive mode, the paper proposes a method for selecting a suitable video guide to be displayed to a novice user indicating how to use an object, purely triggered by the user’s gaze. The approach is tested on a variety of daily tasks ranging from opening a door, to preparing coffee and operating a gym machine.