Open Access

ARTICLE

DAUNet: Detail-Aware U-Shaped Network for 2D Human Pose Estimation

by Xi Li^1,2, Yuxin Li², Zhenhua Xiao^3,*, Zhenghua Huang¹, Lianying Zou¹

1 College of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, 330108, China
2 School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
3 School of Computer Science and Technology, Hubei Business College, Wuhan, 430079, China

* Corresponding Author: Zhenhua Xiao. Email:

Computers, Materials & Continua 2024, 81(2), 3325-3349. https://doi.org/10.32604/cmc.2024.056464

Received 23 July 2024; Accepted 07 October 2024; Issue published 18 November 2024

Abstract

Human pose estimation is a critical research area in the field of computer vision, playing a significant role in applications such as human-computer interaction, behavior analysis, and action recognition. In this paper, we propose a U-shaped keypoint detection network (DAUNet) based on an improved ResNet subsampling structure and spatial grouping mechanism. This network addresses key challenges in traditional methods, such as information loss, large network redundancy, and insufficient sensitivity to low-resolution features. DAUNet is composed of three main components. First, we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature loss. Second, after upsampling, the network eliminates redundant features, improving the overall efficiency. Finally, a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map, allowing for better restoration of the original image size and higher accuracy. Experimental results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models, with a mean PCKh@0.5 score of 91.6% on the MPII dataset and an AP of 76.1% on the COCO dataset. Moreover, real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments, highlighting its potential for broader applications.

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Keywords

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