No abstract

An autostereoscopic display provides users great enjoyment of stereo visualization without uncomfortable and inconvenient drawbacks of wearing stereo glasses. However, bandwidth constraints of current multi-view 3D display severely restrict the number of views that can be simultaneously displayed without degrading resolution or increasing display cost unacceptably. An alternative to multiple view presentation is that the position of observer can be measured by using viewer-tracking sensor. It is a very important module of the viewer-tracking component for fluently rendering and accurately projecting the stereo video. In order to render stereo content with respect to user's view points and to optically project the content onto the left and right eyes of the user accurately, the real-time viewer tracking technique that allows the user to move around freely when watching the autostereoscopic display is developed in this study. It comprises the face detection by using multiple eigenspaces of various lighting conditions, fast block matching for tracking four motion parameters of the user's face region. The Edge Orientation Histogram (EOH) on Real AdaBoost to improve the performance of original AdaBoost algorithm is also applied in this study. The AdaBoost algorithm using Haar feature in OpenCV library developed by Intel to detect human face and enhance the accuracy performance with rotating image. The frame rate of viewer tracking process can achieve up to 15 Hz. Since performance of the viewer tracking autostereoscopic display is still influenced under variant environmental conditions, the accuracy, robustness and efficiency of the viewer-tracking system are evaluated in this study.