【作者】 张山林；

【导师】 申永军；

【作者基本信息】 兰州大学 ， 计算机应用技术， 2013， 硕士

【摘要】 人脸检测与跟踪是人脸信息处理的先期任务之一,同时是人脸信息处理的关键。人脸图像处理的领域除了包含我们熟知的人脸识别之外,通常还包括表情识别、智能人机交互、视频监控等,而几乎每一个研究方向都离不开人脸检测与跟踪。本文在分析和研究了近年来国内外关于人脸检测与跟踪的最新研究成果的基础之上,对人脸检测与跟踪算法进行了系统的研究,针对人脸非刚性的特点以及复杂环境下的跟踪可能遇到目标容易丢失的问题,提出了基于AdaBoost的实时鲁棒人脸检测与跟踪算法,并在实验室环境下验证了该方法。本文主要研究内容包括：1、在研究了大量经典的人脸检测算法的基础上,将基于AdaBoost的人脸检测算法与肤色过滤相融合,从而可以减少AdaBoost算法的启动次数,提高了人脸检测的效率；同时,通过删除多余的Harr矩形特征,增加训练样本数据库,从而提高了人脸检测的准确率,降低了人脸误检率。2、在研究并实际测试了大量跟踪算法的基础上,重点研究和分析了两种人脸跟踪算法：CAMSHIFT和Particle Filter算法。针对人脸跟踪过程中可能出现的复杂背景和遮挡的情况,提出了在正常情况下,使用基于CAMSHIFT全局跟踪器进行全局跟踪；如果人脸信息低于特定阈值,将转用基于Particle Filter的本地跟踪器进行跟踪。3、利用相关的软硬件环境实际模拟测试了本文中设计的检测与跟踪算法,并将本文提出的一些算法与前人的算法进行了对比和分析,主要对比的是检测与跟踪时间以及实际的检测与跟踪效果。更多还原

【Abstract】 Face detection and tracking is not only a preliminary task for face information processing, but also a key step to it. In addition to the well-known face recognition, the fields of the face image processing usually include expression recognition, intelligent human-computer interaction and video surveillance, and almost every field is inseparable from the face detection and tracking. This paper based on the latest research results for face detection and tracking at home and abroad, systematic analyses and study the face detection and tracking algorithms. According to problems, such as the face’s variability and the tracker may lose the target easily in complicated environment, algorithm based on improved AdaBoost is proposed for face detection and tracking, which is verified real-time performance and robustness under the experimental condition. The main research results of this paper can be concluded as follows:1. Research a large number of classic face detection algorithms, we combined the face detection algorithm based on AdaBoost with color filter method. By this way, the start-up times of the AdaBoost algorithm goes down and the efficiency of the face detection would be improved. In addition, we reduced the face detection error rate through removing redundant Harr-like rectangle features and inreasing the training sample.2. Study and actual test a large number of tracking algorithms, we focus on the following two algorithms:the CAMSHIFT and Particle Filter algorithm. According to the complex background and occlusion in the process of face tracking, we put forward that under normal circumstances, the global tracker based on CAMSHIFT is called for tracking, once the face information below a specific threshold value, the local tracker base on Particle Filter is invoked to face tracking.3. We simulate and test the face detection and tracking algorithm we designed in this paper under certain hardware and software environment, at the same time, we compared the proposed algorithm with previous algorithm in time complexity and the actual effect on the face detection and tracking.更多还原