基于时空上下文和随机森林的人眼跟踪定位算法研究

为了得到人眼跟踪过程中更好的鲁棒性和实时性以及跟踪精度,提出一种基于自适应增强分类算法(AdaBoost)、随机森林(RF)和时空上下文(STC)的重定位跟踪算法。该算法结构分为3层,分别为AdaBoost人脸检测、STC人脸跟踪和RF人眼定位。首先,利用AdaBoost在第一帧识别出人脸,从而提取出人脸窗口。接着,使用时空上下文跟踪算法进行人脸跟踪。然后,联合定向梯度直方图(HOG)算法进行相似度判断,以达到目标丢失后继续跟踪的目的。最后,采用随机森林算法进行人眼定位。实验结果表明,与传统的随机森林人眼跟踪算法相比,该算法在跟踪速度达到原方法的2倍左右,并在跟踪精度和鲁棒性上和原算法相同。基本满足在裸眼3D显示时人脸跟踪和人眼定位的精度高、实时性快、鲁棒性好的要求。

Human eye locating and tracking using space-time context and random forest

In order to obtain better robustness, real-time and tracking accuracy in human eye tracking process, this paper proposed a relocation tracking method based on adaptive boosting (AdaBoost), random forest (RF) and space-time context (STC). The algorithm structure was divided into three layers, which was AdaBoost face detection, STC face tracking and RF eye positioning. First, AdaBoost was used to recognize faces in the first frame to extract face windows. Then, the spatio-temporal context algorithm was employed for face tracking. Afterwards, the histogram of oriented gradient (HOG) was used to judge the similarity, so as to achieve the goal of tracking after the target is lost. Finally, the random forest algorithm was used to locate the human eye. Experimental results indicate that the algorithm has a tracking speed of about 2 times as much as the original method. Moreover, this method had the same tracking accuracy and robustness as the original algorithm. It can satisfy high precision for human eye location, real-time, good robustness in naked eye 3D display.