A study of human pose estimation in low-light environments using YOLOv8 model

Abstract

Human pose estimation is a formidable task in the field of computer vision., often constrained by limited training samples and various complexities encountered during target detection, including complex backgrounds, object occlusion, crowded scenes, and varying perspectives. The primary objective of this research paper is to explore the performance disparities of the recently introduced YOLOv8 model in the context of human pose estimation. We conduct a comprehensive evaluation of six different models with varying complexities on the same low-light photograph to assess their precision and speed. The objective is to determine the suitability of each model for specific environmental contexts. The experimental results reveal that our findings demonstrate a partial regression in accuracy for the yolov8s-pose and yolov8m-pose models when tested on our sampled images. The increase in model layers indicates enhanced complexity and expressive power, while additional parameters signify improved learning capabilities at the expense of increased computational resource requirements.