基于SURF改进Camshift的目标跟踪方法

Jianhong Li, Ji Zhang, Zhen Zhou, Wei Guo, Bo Wang, Qingjie Zhao
{"title":"基于SURF改进Camshift的目标跟踪方法","authors":"Jianhong Li, Ji Zhang, Zhen Zhou, Wei Guo, Bo Wang, Qingjie Zhao","doi":"10.1109/OSSC.2011.6184709","DOIUrl":null,"url":null,"abstract":"Camshift is an effective algorithm for real time dynamic target tracking applications, which only uses color features and is sensitive to illumination and some other environment factors. When similar color existing in the background, traditional Camshift algorithm may fail, that is the target getting lost. To solve the problem, an improved Camshift algorithm is firstly proposed in this paper to reduce the influence of illumination interference. Besides, a method judging whether the target is lost is also proposed. Once the target is judged lost, the Speeded Up Robust Features (SURF) is utilized to find it again and the improved Camshift keeps on tracking the target continuously. SURF is invariant to scale, rotation and translation of images. We program in C++ based on OpenCV. The results prove that the proposed method is more robust than the traditional Camshift and give better tracking performance than some other improved methods.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Object tracking using improved Camshift with SURF method\",\"authors\":\"Jianhong Li, Ji Zhang, Zhen Zhou, Wei Guo, Bo Wang, Qingjie Zhao\",\"doi\":\"10.1109/OSSC.2011.6184709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Camshift is an effective algorithm for real time dynamic target tracking applications, which only uses color features and is sensitive to illumination and some other environment factors. When similar color existing in the background, traditional Camshift algorithm may fail, that is the target getting lost. To solve the problem, an improved Camshift algorithm is firstly proposed in this paper to reduce the influence of illumination interference. Besides, a method judging whether the target is lost is also proposed. Once the target is judged lost, the Speeded Up Robust Features (SURF) is utilized to find it again and the improved Camshift keeps on tracking the target continuously. SURF is invariant to scale, rotation and translation of images. We program in C++ based on OpenCV. The results prove that the proposed method is more robust than the traditional Camshift and give better tracking performance than some other improved methods.\",\"PeriodicalId\":197116,\"journal\":{\"name\":\"2011 IEEE International Workshop on Open-source Software for Scientific Computation\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Workshop on Open-source Software for Scientific Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OSSC.2011.6184709\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OSSC.2011.6184709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

摘要

Camshift算法仅利用颜色特征,对光照等环境因素敏感,是一种有效的实时动态目标跟踪算法。当背景中存在相似的颜色时,传统的Camshift算法可能会失败,即目标丢失。为了解决这一问题,本文首先提出了一种改进的Camshift算法,以减少光照干扰的影响。此外,还提出了一种判断目标是否丢失的方法。一旦判断目标丢失,利用加速鲁棒特征(SURF)重新找到目标,改进的Camshift继续对目标进行连续跟踪。SURF对图像的缩放、旋转和平移是不变的。我们在OpenCV的基础上使用c++编程。结果表明,该方法比传统的Camshift方法具有更强的鲁棒性,并且具有更好的跟踪性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Object tracking using improved Camshift with SURF method
Camshift is an effective algorithm for real time dynamic target tracking applications, which only uses color features and is sensitive to illumination and some other environment factors. When similar color existing in the background, traditional Camshift algorithm may fail, that is the target getting lost. To solve the problem, an improved Camshift algorithm is firstly proposed in this paper to reduce the influence of illumination interference. Besides, a method judging whether the target is lost is also proposed. Once the target is judged lost, the Speeded Up Robust Features (SURF) is utilized to find it again and the improved Camshift keeps on tracking the target continuously. SURF is invariant to scale, rotation and translation of images. We program in C++ based on OpenCV. The results prove that the proposed method is more robust than the traditional Camshift and give better tracking performance than some other improved methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Optimization research of genetic neural network based on Scilab Towards open machine learning: Mloss.org and mldata.org A Scilab toolbox of nonlinear regression models using a linear solver Multi-agent collaboration based trust routing scheme for military ad hoc network Design and implement of the OFDM communication system
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1