We have created a framework which uses a Kalman Filter to predict future PAtFs in order to compensate for the delay/lag and to reduce the bandwidth/creation burden of any visual multimedia. ASL’s desktop/ remote eye tracking system is used by more researchers than any other desktop/remote eye tracking system. This delay reduces the benefits of using a PAtF due to the fact that the person’s attention area can change drastically during the delay period, thus increasing the probability of peripheral image quality reduction being detected. Multimedia transmission through a network introduces a delay. ASL is the first company to develop head mounted optics, eye/head integration, parallax free optics. One of these properties is the use of eye gaze to mark linguistic contrasts. The peripheral image quality can be decreased without a viewer noticing image quality reduction. scientist in 1962, ASL developed the first video based eye tracker in 1974.
This is possible due to the fact that the human visual system has limited perception capabilities and only 2 degrees out of the total of 180 provide the highest quality of perception. The PyLink toolkit includes Pylink module, which implements all core EyeLink. It is most powerful when used with the Ethernet link interface, which allows remote control of data collection and real-time data transfer. The concept of the PAtF allows significant reduction of the bandwidth of a video stream and computational burden reduction in the case of 3D media creation and transmission. The EyeLink® eye-tracking system is designed to implement most of the required software base for data collection and conversion. In this paper we propose an algorithm for predicting a person’s perceptual attention focus (PAtF) through the use of a Kalman Filter design of the human visual system.
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