A Coupled Hidden Markov Random Field model for simultaneous face clustering and tracking in videos

A Coupled Hidden Markov Random Field model for simultaneous face clustering and tracking in videos
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用于视频中同时人脸聚类和跟踪的耦合隐马尔可夫随机场模型

DOI:
10.1016/j.patcog.2016.10.022
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发表时间:
2017-04
影响因子:
8
通讯作者:
Ji Qiang
Ji Qiang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Wu Baoyuan;Hu Bao-Gang;Ji Qiang

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人脸聚类和人脸跟踪是人脸视频自动处理中的两个研究热点。然而,尽管它们之间存在内在联系,但它们长期以来一直被分开研究。在本文中,我们提出在真实世界的视频中同时进行人脸聚类和人脸跟踪。研究的动机是人脸聚类和人脸跟踪可以相互提供有用的信息和约束,从而可以相互引导和提高性能。为此,我们引入了一个耦合隐马尔可夫随机场(CHMRF)来同时模拟人脸聚类、人脸跟踪及其相互作用。提出了一种基于约束聚类和最优跟踪的有效算法,实现了聚类标签和人脸跟踪的联合优化。我们在几个视频上展示了对最先进的人脸聚类和跟踪结果的显著改进。
Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.
DOI: 10.1007/978-3-642-33786-4_13
发表时间: 2012-10
期刊: --
影响因子: --
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