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This project focuses on the creation of a new type of egocentric (first person) vision dataset. For that purpose, the EgoMon Gaze & Video Dataset is presented. This EgoMon dataset was recorded using the eye gaze tracking technology that studies the movement and position of the eyes. The Tobii glasses (wearable, eye tracker and head-mounted device) were the main tool used to record and extract the gaze data for this dataset. The dataset consists in 7 videos of 34 minutes each one of average, 13428 frames extracted from each video (with a frequency of 1 fps), and 7 files with the gaze data (fixations points of the wearer of the glasses) for each frame and video. The videos were recorded in the city of Dublin (Ireland) both indoor and outdoor. The generated dataset has been used to evaluate the performance of a state of art model for visual saliency prediction on egocentric video.