[1]骆绍烨,刘丽桑.基于改进Camshift的人脸跟踪算法[J].延边大学学报(自然科学版),2017,43(02):144-149.
　LUO Shaoye,LIU Lisang.Face tracking algorithm based on improved Camshift[J].Journal of Yanbian University,2017,43(02):144-149.
点击复制

基于改进Camshift的人脸跟踪算法

《延边大学学报(自然科学版)》[ISSN:1004-4353/CN:22-1191/N] 卷: 第43卷 期数: 2017年02期 页码: 144-149 栏目: 基础科学研究 出版日期: 2017-07-20
Title:
Face tracking algorithm based on improved Camshift
作者:
骆绍烨1;  刘丽桑2
1.莆田学院 信息工程学院, 福建 莆田 351100; 2.福建工程学院 信息科学与工程学院, 福建 福州 350118
Author(s):
LUO Shaoye1;  LIU Lisang2
1.College of Information Engineering, Putian University, Putian 351100, China; 2.College of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
关键词:
人脸跟踪;  Camshift;  分块颜色直方图;  Kalman滤波
Keywords:
face tracking;  Camshift;  block color histogram;  Kalman filter
分类号:
TP391.41
文献标志码:
A
摘要:
研究了一种基于改进Camshift的人脸跟踪算法.该算法采用分块加权的直方图匹配方法增强人脸的辨识度,通过人脸形态约束筛选不合理的人脸形态变化,并结合Kalman滤波器预测修正跟踪人脸位置.实验表明,改进后的算法比经典Camshift算法有更强的抗肤色干扰能力和跟踪准确性.
Abstract:
The improved Camshift algorithm for face tracking is proposed, which enhances the identification of facial structure by using the block weighted color histogram in template matching, filtrates the unreasonable facial morphological change according to the face shape detection, predicts and amends the accuracy of face tracking through Kalman filter. Experimental results prove the improved algorithm become stronger interference ability, higher tracking accuracy than the classical Camshift algorithm.

参考文献/References:

[1] Azarbayejani A, Horowitz B, Pentland A. Recursive estimation of structure and motion using relative orientation constraints[J]. Proc IEEE CVPR, 1993:294-299.
[2] Eleftheriadis A, Jacquin A. Automatic face location detection and tracking for model-assisted coding of video teleconferencing sequences at low bit-rates[J]. Signal Processing Image Communication, 1995,7(3):231-248.
[3] Fukunaga K, Hostetler L D. The estimation of the gradient of a density function, with applications in pattern recognition[J]. IEEE Trans Information Theory, 1975,21(1):32-40.
[4] 梁路宏,艾海舟.基于人脸检测的人脸跟踪算法[J].计算机工程与应用,2001(17):42-45.
[5] Bradski G R. Computer vision face tracking as a component of a perceptual user interface[C]//Proceedings Fourth IEEE Workshop on Applications of Computer Vision. Princeton, New Jersey, USA, 1998:214-219.
[6] 贾亮亮.基于视频序列的运动目标检测与跟踪算法研究[D].重庆:重庆大学,2011.
[7] Kailath T. The divergence and Bhattacharyya distance measures in signal selection[J]. IEEE Transactions on Communication Technology, 1967,15(1):52-60.
[8] 张丽媛,梁凤梅.改进的CamShift人脸跟踪算法[J].科学技术和工程,2014,14(13):231-235.
[9] 黄亚勤,董秀成,李郝,等.改进的CamShift人脸跟踪算法[J].计算机工程,2011,37(2):180-182.
[10] 林建华,刘党辉,邵显奎.多特征融合的Camshift算法及其进一步改进[J].计算机应用,2012,32(10):2814-2816.
[11] Baumberg A M, Hogg D C. Learning spatiotemporal models from training examples[C]//Proc British Machine Vision Conference, Birmingham, 1995:63-75.
[12] 胡铟,杨静宇.基于分块颜色直方图的MeanShift 跟踪算法[J].系统仿真学报,2009,21(10):2936-2939.
[13] 王丽,郝晓丽.基于Kalman滤波器和改进Camshift算法的双眼跟踪[J].微电子学与计算机,2016,33(6):109-112.

相似文献/References:

[1]王齐,金小峰.车辆识别与样本自动采集方法的研究[J].延边大学学报(自然科学版),2015,41(02):164.
　WANG Qi,JIN Xiaofeng*.Research on method of vehicle detection and sample acquisition[J].Journal of Yanbian University,2015,41(02):164.
[2]骆绍烨,刘丽桑.基于特征组合的多人脸跟踪算法[J].延边大学学报(自然科学版),2018,44(01):63.
　LUO Shaoye,LIU Lisang.Multi-face tracking algorithm based on feature combination[J].Journal of Yanbian University,2018,44(02):63.

备注/Memo

收稿日期: 2017-03-31 作者简介: 骆绍烨(1982—),男,讲师,研究方向为计算机视觉、信息检索.
基金项目: 国家自然科学基金资助项目(81373552); 莆田市科技计划项目(2015G2014)

更新日期/Last Update: 2017-06-20