Mobile Device Eye Tracking on Dynamic Visual Contents using Edge Computing and Deep Learning

Abstract

Eye-tracking has been used in various domains, including human-computer interaction, psychology, and many others. Compared to commercial eye trackers, eye tracking using off-the-shelf cameras has many advantages, such as lower cost, pervasiveness, and mobility. Quantifying human attention on the mobile device is invaluable in human-computer interaction. Like videos and mobile games, dynamic visual stimuli require higher attention than static visual stimuli such as web pages and images. This research aims to develop an accurate eye-tracking algorithm using the front-facing camera of mobile devices to identify human attention hotspots when viewing video type contents. The shortage of computational power in mobile devices becomes a challenge to obtain higher user satisfaction. Edge computing moves the processing power closer to the source of the data and reduces the latency introduced by the cloud computing. Therefore, the proposed algorithm will be extended with mobile edge computing to provide a real-time eye tracking experience for users