Deep-pipelined FPGA implementation of ellipse estimation for eye tracking
Keisuke Dohi, Yuma Hatanaka, Negi Kazuhiro, Yuichiro Shibata, Kiyoshi Oguri · 2012
This paper presents a deep-pipelined FPGA implementation of real-time ellipse estimation for eye tracking. The system is constructed by the Starburst algorithm on a stream-oriented architecture and the RANSAC algorithm without any external memories. In particular, the paper presents comparative results between three different hypothesis generators for the RANSAC algorithm based on Cramer's rule, Gauss-Jordan elimination and LU decomposition. Comparison criteria include resource usage, throughput and energy consumption. The result shows that the three implementations have different characteristics and the optimal algorithm needs to be chosen depending on the amount of resources on FPGAs and required performance.