中国科学院光电技术研究所机构知识库
Advanced  
IOE OpenIR  > 光电探测与信号处理研究室(五室)  > 会议论文
题名:
An on-line learning tracking of non-rigid target combining multipleinstance boosting and level set
作者: Chen, Mingming1,2; Cai, Jingju1
出版日期: 2013
会议名称: Proceedings of SPIE: MIPPR 2013: Automatic Target Recognition and Navigation
会议日期: 2013
学科分类: Automatic target recognition - Image segmentation - Learning algorithms
DOI: 10.1117/12.2031170
中文摘要: Visual tracking algorithms based on online boosting generally use a rectangular bounding box to represent the position of the target, while actually the shape of the target is always irregular. This will cause the classifier to learn the features of the non-target parts in the rectangle region, thereby the performance of the classifier is reduced, and drift would happen. To avoid the limitations of the bounding-box, we propose a novel tracking-by-detection algorithm involving the level set segmentation, which ensures the classifier only learn the features of the real target area in the tracking box. Because the shape of the target only changes a little between two adjacent frames and the current level set algorithm can avoid the re-initialization of the signed distance function, it only takes a few iterations to converge to the position of the target contour in the next frame. We also make some improvement on the level set energy function so that the zero level set would have less possible to converge to the false contour. In addition, we use gradient boost to improve the original multi-instance learning (MIL) algorithm like the WMILtracker, which greatly speed up the tracker. Our algorithm outperforms the original MILtracker both on speed and precision. Compared with the WMILtracker, our algorithm runs at a almost same speed, but we can avoid the drift caused by background learning, so the precision is better. © 2013 SPIE.
英文摘要: Visual tracking algorithms based on online boosting generally use a rectangular bounding box to represent the position of the target, while actually the shape of the target is always irregular. This will cause the classifier to learn the features of the non-target parts in the rectangle region, thereby the performance of the classifier is reduced, and drift would happen. To avoid the limitations of the bounding-box, we propose a novel tracking-by-detection algorithm involving the level set segmentation, which ensures the classifier only learn the features of the real target area in the tracking box. Because the shape of the target only changes a little between two adjacent frames and the current level set algorithm can avoid the re-initialization of the signed distance function, it only takes a few iterations to converge to the position of the target contour in the next frame. We also make some improvement on the level set energy function so that the zero level set would have less possible to converge to the false contour. In addition, we use gradient boost to improve the original multi-instance learning (MIL) algorithm like the WMILtracker, which greatly speed up the tracker. Our algorithm outperforms the original MILtracker both on speed and precision. Compared with the WMILtracker, our algorithm runs at a almost same speed, but we can avoid the drift caused by background learning, so the precision is better. © 2013 SPIE.
收录类别: Ei
语种: 英语
卷号: 8918
ISSN号: 0277786X
文章类型: 会议论文
页码: 891802
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.ioe.ac.cn/handle/181551/7700
Appears in Collections:光电探测与信号处理研究室(五室)_会议论文

Files in This Item:
File Name/ File Size Content Type Version Access License
2013-2099.pdf(589KB)会议论文--限制开放View 联系获取全文

作者单位: 1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan Province, 610209, China
2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

Recommended Citation:
Chen, Mingming,Cai, Jingju. An on-line learning tracking of non-rigid target combining multipleinstance boosting and level set[C]. 见:Proceedings of SPIE: MIPPR 2013: Automatic Target Recognition and Navigation. 2013.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Chen, Mingming]'s Articles
[Cai, Jingju]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Chen, Mingming]‘s Articles
[Cai, Jingju]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: 2013-2099.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2016  中国科学院光电技术研究所 - Feedback
Powered by CSpace