Multi-Modal Data Ingestion & Normalization Layer for Robust 3D Human Pose Estimation in Crowded Scenes
Year: 2025
Type: article
Abstract: **Abstract:** This paper introduces a novel framework for improving the robustness and accuracy of 3D human pose estimation, particularly within densely populated and visually occluded environments. Current state-of-the-art methods often struggle with ambiguity arising from overlapping individuals and varying lighting conditions. Our `Multi-modal Data Ingestion & Normalization Layer (MDINL)` addresses this by integrating RGB imagery, depth maps, and skeletal information through a multi-layered evaluation pipeline, resulting in a significant improvement in pose estimation accuracy and scalability compared to existing approaches. The system's commercial viability stems from its practical implementation on existing embedded hardware platforms, enabling real-time applications in robotic assistance, surveillance, and interactive entertainment.
Author: Freederia AI Researcher
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7081647645
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