Study on Improvement of Estimation Accuracy in Pose Estimation Model Using Time Series Correlation
Study on Improvement of Estimation Accuracy in Pose Estimation Model Using Time Series Correlation
复制标题
利用时间序列相关性提高位姿估计模型估计精度的研究
DOI：
10.1109/gcce50665.2020.9291962
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Takaaki Ishikawa and Hiroshi Watanabe
中科院分区：
文献类型：
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作者：
Atsuya Yamakawa;Takaaki Ishikawa and Hiroshi Watanabe
在视频中检测人体姿势是一项困难的任务。虽然在过去的几年里已经提出了许多高性能的人体姿态估计模型，估计精度一直是一个主要的问题。在这项研究中，我们提出了一种方法，以提高视频的人体姿态估计的准确性。从技术上讲，预测的人体姿势是一组时间序列数据。因此，通过使用时间序列相关性，可以以更好的精度执行人体姿势估计。我们将基于CNN的人体姿态估计模型与多目标跟踪框架相结合来实现这一目标。未检测到/误检测到的身体关节将使用来自先前帧和后续帧的信息进行插值。因此，我们提出的方法提高了现有的基于CNN的人体姿态估计模型的准确性，将未检测到的帧和误检测到的帧的数量分别减少了6.30%和0.98%。
Detecting human pose in a video is a difficult task. Although many high-performed human pose estimation models have been proposed in the last few years, the estimation accuracy has always been a major concern. In this study we present a method to improve the accuracy of human pose estimation for videos. Technically, predicted human pose is a set of time series data. Thus, by using time series correlation, human pose estimation can be performed in a better accuracy. We combine a CNN based human pose estimation model with a multiple object tracking framework to achieve this. Undetected/mis-detected body joints will be interpolated using the information from previous and following frames. As a result, our proposed method improved the accuracy of an existing CNN based human pose estimation model by reducing the number of undetected and mis-detected frames by 6.30% and 0.98% respectively.