Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

TL;DR

This paper introduces a transfer learning approach using MobileNetV3-Small and ViTPose for human keypoint detection in low-resolution thermal images, specifically applied to the TUG test, outperforming traditional methods in accuracy and efficiency.

Contribution

It is the first to apply thermal image-based TUG test analysis with a novel transfer learning model combining MobileNetV3-Small and ViTPose, advancing mobility assessment techniques.

Findings

Achieved high AP scores of 0.861, 0.942, and 0.887 on keypoint detection.

Outperformed traditional models like Mask R-CNN and ViTPose-Base.

Demonstrated superior computational efficiency with fewer parameters and FLOPS.

Abstract

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring.