摘要
提出基于在线增量式极端随机森林分类器的实时人脸跟踪算法。算法用在线极端随机森林分类器实现基于检测的跟踪,并结合动态目标框架和P-N学习矫正检测的错误。实验结果表明,该算法能够在不确定背景下对任意人脸实现较长时间段内的稳定快速的实时跟踪,并能有效排除背景等的干扰,效果较好。
The paper proposes a real-time face tracking algorithm,which is based on online incremental extremely random forests classifier. The algorithm achieves detection-based real-time tracking using online incremental extremely random forests classifier,and combines dynamic target framework and P-N learning to correct detection errors. Experimental results show,the proposed algorithm can realise fast and stable real-time tracking for any face in a longer period under uncertain background, and can effectively overcome interferences such as background with preferable effect.
出处
《计算机应用与软件》
CSCD
2016年第5期270-273,297,共5页
Computer Applications and Software
基金
江苏省自然科学基金项目(BK2012128)
关键词
在线增量学习
极端随机森林
P-N学习
动态目标框架
实时人脸跟踪
Online incremental learning
Extremely random forests
P-N learning
Dynamic target framework
Real-time face tracking
