ClickTrack: Towards real-time interactive single object tracking

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-11-26 DOI:10.1016/j.patcog.2024.111211
Kuiran Wang , Xuehui Yu , Wenwen Yu , Guorong Li , Xiangyuan Lan , Qixiang Ye , Jianbin Jiao , Zhenjun Han
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Abstract

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.
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ClickTrack:走向实时交互单对象跟踪
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。
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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.
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