基于网格特征的视觉跟踪

2011 IEEE International Conference on Computer Science and Automation Engineering Pub Date : 2011-06-10 DOI:10.1109/CSAE.2011.5952844

Yi Zhou, H. Snoussi, Shibao Zheng

{"title":"基于网格特征的视觉跟踪","authors":"Yi Zhou, H. Snoussi, Shibao Zheng","doi":"10.1109/CSAE.2011.5952844","DOIUrl":null,"url":null,"abstract":"Vulnerability to occlusion is one of the main issue in visual tracking. In this proposal, we exploit the local grid features to build a robust tracker. To improve performance under occlusion, local and global features are modeled for a target tracking. Cooperating with the novel features, a new segmentation and similarity measurement are proposed for exploring the local grid advantages. Experimental results show that our tracker outperforms other two effective visual tracking methods under occlusion.","PeriodicalId":138215,"journal":{"name":"2011 IEEE International Conference on Computer Science and Automation Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Grid features based visual tracking\",\"authors\":\"Yi Zhou, H. Snoussi, Shibao Zheng\",\"doi\":\"10.1109/CSAE.2011.5952844\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vulnerability to occlusion is one of the main issue in visual tracking. In this proposal, we exploit the local grid features to build a robust tracker. To improve performance under occlusion, local and global features are modeled for a target tracking. Cooperating with the novel features, a new segmentation and similarity measurement are proposed for exploring the local grid advantages. Experimental results show that our tracker outperforms other two effective visual tracking methods under occlusion.\",\"PeriodicalId\":138215,\"journal\":{\"name\":\"2011 IEEE International Conference on Computer Science and Automation Engineering\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Computer Science and Automation Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSAE.2011.5952844\",\"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 Conference on Computer Science and Automation Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSAE.2011.5952844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

遮挡脆弱性是视觉跟踪中的主要问题之一。在这个建议中，我们利用局部网格特征来构建一个鲁棒跟踪器。为了提高遮挡下的性能，对目标跟踪进行了局部和全局特征建模。结合这些新的特征，提出了一种新的分割和相似度度量方法来挖掘局部网格的优势。实验结果表明，该跟踪器在遮挡下的性能优于其他两种有效的视觉跟踪方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Grid features based visual tracking

Vulnerability to occlusion is one of the main issue in visual tracking. In this proposal, we exploit the local grid features to build a robust tracker. To improve performance under occlusion, local and global features are modeled for a target tracking. Cooperating with the novel features, a new segmentation and similarity measurement are proposed for exploring the local grid advantages. Experimental results show that our tracker outperforms other two effective visual tracking methods under occlusion.

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

2011 IEEE International Conference on Computer Science and Automation Engineering

自引率

0.00%

发文量