SPKDB-Net:一种基于显著部分姿态关键点的重复动作计数双分支网络

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jinying Wu , Jun Li , Qiming Li
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引用次数: 0

摘要

随着深度学习的不断发展,重复动作计数领域逐渐受到众多研究者的关注。利用人体姿态估计网络提取姿态关键点被证明是一种有效的姿态级方法。然而,现有的姿态级方法存在一些缺陷,例如忽略了视频中遮挡和不利的视角会影响姿态关键点提取的准确性。为了克服这些问题,我们提出了一种简单而高效的基于突出部分姿态关键点的双分支网络(SPKDB-Net)。具体来说,我们设计了一个双分支输入通道,包括一个全局输入分支和一个显著部分输入分支。基于全局的输入分支用于输入由人体姿态估计网络提取的全身姿态关键点,突出部分输入分支用于输入突出部分姿态关键点(即头、肩、手)。第二个分支作为第一个分支的辅助,从而有效地解决外部因素的影响。此外,我们提出了一种DFEPM-Module,通过注意机制获得姿态关键点之间的远距离依赖关系,并通过卷积获得由注意机制融合的显著局部特征。最终,在具有挑战性的RepCount-pose, UCFRep-pose和Countix-Fitness-pose基准上进行的大量实验表明,我们提出的SPKDB-Net达到了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SPKDB-Net: A Salient-Part Pose Keypoints-Based Dual-Branch Network for repetitive action counting
With the continuous development of deep learning, the field of repetitive action counting is gradually gaining notice from many researchers. Extraction of pose keypoints using human pose estimation networks is proven to be an effective pose-level method. However, the existing pose-level methods have some drawbacks, for example, ignoring the fact that occlusion and unfavourable viewing angles in videos lead to affect the accuracy of pose keypoints extraction. To overcome these problems, we propose a simple but efficient Salient-Part Pose Keypoints-Based Dual-Branch Network (SPKDB-Net). Specifically, we design a dual-branch input channel consisting of a global-based and a salient-part input branch. The global-based input branch is used to input the pose keypoints of the whole body extracted by the human pose estimation network, and the salient-part input branch is used to input the salient-part pose keypoints (i.e., head, shoulders, and hands). The second branch acts as an auxiliary to the first branch, thus effectively addressing the influence of external factors. In addition, we propose a DFEPM-Module that obtains long-distance dependency between pose keypoints through the attention mechanism, and obtains salient local features fused by the attention mechanism through convolution. Eventually, extensive experiments on the challenging RepCount-pose, UCFRep-pose and Countix-Fitness-pose benchmarks show that our proposed SPKDB-Net achieves state-of-the-art performance.
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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