应用Camshift跟踪算法提高视频中人脸检测速度

在OpenCV中提供的CascadeClassifier级联分类器利用Haar特征进行人脸检测时，检测速度很慢无法满足视频对实时性的要求，而且光照的影响也很大.基于这两点提出一种新的人脸检测算法，采用Camshift目标跟踪与人脸检测相结合提高检测速度并利用直方图均衡化减弱光照的影响.该算法首先把CascadeClassifier级联分类器方法检测到的人脸区域设为ROI区域，对ROI区域操作并用Camshift算法进行目标跟踪，其次要定时进行一次人脸检测用来更新ROI区域保证跟踪的准确性.经过实验结果分析表明：利用改进后的算法，人脸检测的速度有明显提高（约为40%），并且减小了光照的影响.

Improving Face Detection Speed in Video Using Camshift Tracking Algorithm

When the CascadeClassifier cascade classifier provided in OpenCV uses Haar features for face detection, the detection speed is too slow to meet the real-time requirements of the video, and the impact of lighting is also great. Based on these two points, a new face detection algorithm is proposed, which uses Camshift target tracking and face detection to improve the detection speed and uses histogram equalization to reduce the impact of light. The algorithm first sets the face area detected by the CascadeClassifier cascade classifier method as the ROI area, operates on the ROI area and uses the Camshift algorithm for target tracking, and secondly performs face detection regularly to update the ROI area to ensure the tracking accuracy. The analysis of the experimental results shows that: with the improved algorithm, the speed of face detection has been significantly increased (about 40%), and the impact of light is reduced.