跳跃式监视
人工智能
计算机科学
姿势
卷积神经网络
计算机视觉
模式识别(心理学)
图像(数学)
像素
估计
协议(科学)
医学
病理
经济
管理
替代医学
作者
Hung‐Cuong Nguyen,Thi-Hao Nguyen,Rafał Scherer,Jakub Nowak,Agnieszka Siwocha,Van-Hung Le
出处
期刊:Journal of Artificial Intelligence and Soft Computing Research
[De Gruyter]
日期:2022-10-01
卷期号:12 (4): 281-298
被引量:5
标识
DOI:10.2478/jaiscr-2022-0019
摘要
Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000 × 1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.
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