Human posture estimation at low resolution
Pages 174 - 181
Abstract
Human pose estimation is an important research direction in the field of computer vision, which is widely used in human-computer interaction, behavior analysis, and intelligent surveillance. Although existing human pose estimation algorithms possess high accuracy for high-resolution images, they perform poorly for low-resolution images that are prevalent in practical applications, and thus are difficult to be widely used in people's daily lives. In this paper, we propose a new end-to-end network framework for accurate human pose estimation of low-resolution images by combining super-resolution assistance and quantization error optimization. In addition, a composite loss function is designed to jointly train the super-resolution network to generate high-resolution images that contribute to human pose estimation instead of simple pre-processing. The experimental results show that the mAP of our method reaches 68.1% and 61.4% on the COCO datasets downsampled to 128×96 and 64×48.
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Index Terms
- Human posture estimation at low resolution
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Published In
July 2023
383 pages
ISBN:9798400707575
DOI:10.1145/3614008
Copyright © 2023 ACM.
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 October 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China and Shanxi Provincial People's Government Jointly Funded Project of China for Coal Base and Low Carbon
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
Conference
SPML 2023
SPML 2023: 2023 6th International Conference on Signal Processing and Machine Learning
July 14 - 16, 2023
Tianjin, China
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