基于人工神经网络动态标定算法的低成本视线追踪系统

In order to reduce the cost of eye-gaze tracking system and simplify the complexity of the calibration algorithm, a low-cost eye-gaze tracking system was developed.The Haar-like feature and skin color combination algorithm were used to detect the human face.The active appearance model (AAM) algorithm and the optical flow method were used to locate and track the face feature points.And the pupil center was detected by the gradient vector method.An artificial neural network dynamic calibration algorithm was proposed to improve the tracking accuracy and robustness.Experiments show that the eye-gaze tracking system not only has better robustness, but also has higher precision.The average error of the system is 1.34° at head rest, and 3.26° at head movement.