EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum

Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware interactive user experience. Explorations toward development of mechanism using eye-tracking data have so far been limited to 2D cases, researchers are yet approach this topic in 3D from spatiotemporal perspective. We present the first Eye-tracking Dataset for Visual Attention modeling Museum, known as EDVAM. In addition, deep learning model is devised tested with EDVAM predict user’s subsequent previous eye movements. This work reference interaction context museums.