ClickTrack:走向实时交互单对象跟踪

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kuiran Wang , Xuehui Yu , Wenwen Yu , Guorong Li , Xiangyuan Lan , Qixiang Ye , Jianbin Jiao , Zhenjun Han
{"title":"ClickTrack:走向实时交互单对象跟踪","authors":"Kuiran Wang ,&nbsp;Xuehui Yu ,&nbsp;Wenwen Yu ,&nbsp;Guorong Li ,&nbsp;Xiangyuan Lan ,&nbsp;Qixiang Ye ,&nbsp;Jianbin Jiao ,&nbsp;Zhenjun Han","doi":"10.1016/j.patcog.2024.111211","DOIUrl":null,"url":null,"abstract":"<div><div>Single object tracking (SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single object trackers and propose a new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios. Moreover, click as an input type inherently lack hierarchical information. To address ambiguity in certain special scenarios, we designed the Guided Click Refiner (GCR), which accepts point and optional textual information as inputs, transforming the point into the bounding box expected by the operator. The bounding box will be used as input of single object trackers. Experiments on LaSOT and GOT-10k benchmarks show that tracker combined with GCR achieves stable performance in real-time interactive scenarios. Furthermore, we explored the integration of GCR into the Segment Anything model (SAM), significantly reducing ambiguity issues when SAM receives point inputs.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"161 ","pages":"Article 111211"},"PeriodicalIF":7.5000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ClickTrack: Towards real-time interactive single object tracking\",\"authors\":\"Kuiran Wang ,&nbsp;Xuehui Yu ,&nbsp;Wenwen Yu ,&nbsp;Guorong Li ,&nbsp;Xiangyuan Lan ,&nbsp;Qixiang Ye ,&nbsp;Jianbin Jiao ,&nbsp;Zhenjun Han\",\"doi\":\"10.1016/j.patcog.2024.111211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Single object tracking (SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single object trackers and propose a new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios. Moreover, click as an input type inherently lack hierarchical information. To address ambiguity in certain special scenarios, we designed the Guided Click Refiner (GCR), which accepts point and optional textual information as inputs, transforming the point into the bounding box expected by the operator. The bounding box will be used as input of single object trackers. Experiments on LaSOT and GOT-10k benchmarks show that tracker combined with GCR achieves stable performance in real-time interactive scenarios. Furthermore, we explored the integration of GCR into the Segment Anything model (SAM), significantly reducing ambiguity issues when SAM receives point inputs.</div></div>\",\"PeriodicalId\":49713,\"journal\":{\"name\":\"Pattern Recognition\",\"volume\":\"161 \",\"pages\":\"Article 111211\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0031320324009622\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320324009622","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

单目标跟踪(SOT)依赖于精确的目标边界框初始化。在本文中,我们重新考虑了当前初始化单目标跟踪器方法的不足,并提出了一种新的单目标跟踪算法范式,ClickTrack,一种在实时场景中使用点击交互的新范式。此外,单击作为输入类型本身缺乏层次信息。为了解决某些特殊场景中的歧义,我们设计了Guided Click Refiner (GCR),它接受点和可选文本信息作为输入,将点转换为操作符期望的边界框。边界框将用作单目标跟踪器的输入。在LaSOT和GOT-10k基准测试上的实验表明,跟踪器结合GCR在实时交互场景下具有稳定的性能。此外,我们探索了将GCR集成到任何片段模型(SAM)中,显著减少SAM接收点输入时的歧义问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ClickTrack: Towards real-time interactive single object tracking
Single object tracking (SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single object trackers and propose a new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios. Moreover, click as an input type inherently lack hierarchical information. To address ambiguity in certain special scenarios, we designed the Guided Click Refiner (GCR), which accepts point and optional textual information as inputs, transforming the point into the bounding box expected by the operator. The bounding box will be used as input of single object trackers. Experiments on LaSOT and GOT-10k benchmarks show that tracker combined with GCR achieves stable performance in real-time interactive scenarios. Furthermore, we explored the integration of GCR into the Segment Anything model (SAM), significantly reducing ambiguity issues when SAM receives point inputs.
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
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信