基于Kanade-Lucas的人眼跟踪算法研究

Research of eye tracking method based on Kanade-Lucas optical algorithm

在基于AdaBoost算法识别驾驶员眼部疲劳状态时,环境光照、视角的频繁变化是影响识别稳定性的重要因素,为此提出了一种基于Kanade-Lucas(K-L)光流跟踪与AdaBoost级联分类器相结合的改进算法。该算法利用AdaBoost算法识别并存储人眼的角点特征,在AdaBoost算法无法正确识别时,利用(K-L)光流跟踪算法跟踪正确识别的角点有效地提高了人眼识别率,降低了误识别率,并降低了重复识别的运算量。

The sudden change of the lighting or view angle will lead to the decreasing of the eye recognition rate very much.So advanced a new algorithm,which combined the Kanade-Lucas optical algorithm （K-L） with the AdaBoost cascade classifier.This algorithm recognized and saved the Harris corner using the AdaBoost algorithm.Those saved corner features would be tracked using K-L algorithm,if the AdaBoost algorithm did not recognize them again.The method does not change the number of cascade classifier and the field of image pixels,but the recognition rate is higher,the misrecognition rate is lower,and reduces the iterate computation of identification efficiently.