A Reinforcement Learning based Eye-Gaze Behavior Tracking

Abstract

In video established eye tracking methods, there are both mechanical and electrical based approaches existing. With the emerging spread of gaze tracking technology in the recent years and its significance in daily life routine, the data content acquired from the eye behavior tracing turn into important. Several research works were proposed to track the behavior of gaze while playing videos. Tracking an eye gaze while playing a dynamic videos consisting of numerous frames is a complex problem which needs excessive computational efforts. To handle such a complex task, this research proposes Reinforcement Learning (RL) based gaze behavior prediction model. These techniques are found to be invasive in nature and for visual attention behavior analysis applications, these invasive eye tracking system is not applicable. Hence the non-invasive eye tracking could be developed by determining the point of gaze based on observed image processing techniques. Some of the prevailing techniques include artificial intelligence, deep learning, and reinforcement learning and so on. Though quite a few research works has been admitted in this research area, there are several challenges existing so far. The suggested learning techniques are found to be computationally complex and time consuming. This current research work intends to propose a deep convolutional reinforcement learning (DC-RL) model for predicting the visual attention behavior of a person over dynamic scenes.