Authors:
Ali Zakir
1
;
Sartaj Salman
1
;
Gibran Benitez-Garcia
1
and
Hiroki Takahashi
1
;
2
Affiliations:
1
Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
;
2
Artificial Intelligence Exploration/Meta-Networking Research Center, The University of Electro-Communications, Tokyo, Japan
Keyword(s):
2D Human Pose Estimation, EBA-PRNetCC, MLP, EBA, COCO Dataset.
Abstract:
In the current era, 2D Human Pose Estimation has emerged as an essential component in advanced Computer Vision tasks, particularly for understanding human behaviors. While challenges such as occlusion and unfavorable lighting conditions persist, the advent of deep learning has significantly strengthened the efficacy of 2D HPE. Yet, traditional 2D heatmap methodologies face quantization errors and demand complex post-processing. Addressing this, we introduce the EBA-PRNetCC model, an innovative coordinate classification approach for 2D HPE, emphasizing improved prediction accuracy and optimized model parameters. Our EBA-PRNetCC model employs a modified ResNet34 framework. A key feature is its head, which includes a dual-layer Multi-Layer Perceptron augmented by the Mish activation function. This design not only improves pose estimation precision but also minimizes model parameters. Integrating the Efficient Bridge Attention Net further enriches feature extraction, granting the model d
eep contextual insights. By enhancing pixel-level discretization, joint localization accuracy is improved. Comprehensive evaluations on the COCO dataset validate our model’s superior accuracy and computational efficiency performance compared to prevailing 2D HPE techniques.
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