Eye Detection for Attention-Based Video Playback

Eye tracking technology has developed as an important part in human-computer interaction while allowing for some automation depending on where a user looks. We conduct a study that tests the use of eye tracking to facilitate video control where the video will pause or play depending on where the user's attention is. We layout some of the ways in which eye-tracking systems can be used in media players, not only for automation but also to enhance accessibility features, heightened user engagement and experiences. All of this indicates that video playback is a meaningful context to integrate eye-trackers into and that eye-tracking potentially changes how users interact with videos and create less distractions and slowdowns when accessing media. Meaning that for example, if head movements are accurately captured the user could access and view videos, without touching the screen. The study also provides next steps for integrating and/or combining eye tracking with artificial intelligence and deep learning to help increase the accuracy and potential usefulness of integrating eye tracking into video playback systems. The paper will also present some examples of how eye tracking technology is being used in education for training prospective medical practitioners and/or simulation around healthcare for use in mental health and disability learning, and in the field of gaming, around behaviours that extra-gaming. The prototype we built used OpenCV and some of the different basic programming libraries. The device eyetracker was the only device input for tracking the user's eye. Our testing of the prototype concluded that it did perform adequately. Under normal operating conditions the prototype can be improved a lot. And as stated previously, beyond media control, the system could used to improve access for people living with disabilities.Page layout.