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Journal Article Data Augmentation Method for Improving the Accuracy of Human Pose Estimation with Cropped Images
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Authors
Soonchan Park, Sang-baek Lee, Jinah Park
Issue Date
2020-08
Citation
Pattern Recognition Letters, v.136, pp.244-250
ISSN
0167-8655
Publisher
Elsevier
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1016/j.patrec.2020.06.015
Project Code
19HS4900, Development of Intelligent Interaction Technology based on Recognition of User's State and Intention for Digital Life, Park Ji Young
Abstract
Neural networks have improved the accuracy of human pose estimation from a single RGB image. However, such estimation remains difficult, especially when the human body is only partially visible due to a limited field of view of the camera or occlusions. In this paper, we introduce a data augmentation method called body-cropping augmentation (BCA), which generalizes the dataset for effective training in human pose estimation. This technique includes the policies of data generation and the training strategy using the augmented data. The experiments with the COCO val2017 dataset with ground-truth bounding boxes show BCA consistently enhances accuracies of state-of-the-art neural networks by an average of 1.08% without any modification to the network architecture. Moreover, the proposed BCA technique effectively reduces the false negatives of localizing keypoints, especially in an input image with a few visible keypoints.
KSP Keywords
Augmentation method, Bounding Box, Data Augmentation, Data generation, Effective training, False negative, Field of view(FOV), Human body, Human pose estimation, Network Architecture, RGB image