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Uniting holistic and part-based attitudes for accurate and robust deep human pose estimation
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2020-07-28 , DOI: 10.1007/s12652-020-02347-7
Faranak Shamsafar , Hossein Ebrahimnezhad

Deep learning has been utilized in many intelligent systems, including computer vision techniques. Human pose estimation is one of the popular tasks in computer vision that has benefited from modern feature learning strategies. In this regard, recent advances propose part-based approaches since pose estimation based on parts can produce more accurate results than when the human shape is considered holistically as one unbreakable, but deformable object. However, in real-word scenarios, problems like occlusion and cluttered background make difficulties in part-based methods. In this paper, we propose to unite the two attitudes of the part-based and the holistic pose predictions to make more accurate and more robust estimations. These two schemes are modeled using convolutional neural networks as regression and classification tasks in order, and are combined in three frameworks: multitasking, series, and parallel. Each of these settings has its own advantages, and the experimental results on the LSP test set demonstrate that it is essential to observe subjects, both based on parts and holistically in order to achieve more accurate and more robust estimation of human pose in challenging scenarios.



中文翻译:

结合整体和基于零件的态度,以进行准确而可靠的深度人体姿势估计

深度学习已用于许多智能系统中,包括计算机视觉技术。人体姿势估计是计算机视觉中的热门任务之一,受益于现代特征学习策略。在这方面,最近的进展提出了基于零件的方法,因为基于零件的姿态估计可以产生比将人的形状整体上视为一个牢不可破但可变形的物体时更准确的结果。但是,在实词场景中,诸如遮挡和背景混乱之类的问题使基于零件的方法变得困难。在本文中,我们建议将基于零件的姿势和整体姿势预测的两种态度结合起来,以做出更准确,更可靠的估计。使用卷积神经网络作为回归和分类任务依次对这两种方案进行建模,并结合在三个框架中:多任务,串行和并行。这些设置中的每一种都有其自身的优势,并且LSP测试仪上的实验结果表明,有必要基于部分和整体地观察对象,以在挑战性场景中获得更准确,更可靠的人体姿势估计。

更新日期:2020-07-29
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