The research and implementation of an efficient human pose estimation algorithm based on deep learning

• 1、State Key Laboratory of Networking and Switching Technology,Beijing University of Post and Telecommunication,Beijing 100876, Beijing, 100876

Abstract：To address the challenge of the current human pose estimation algorithm models being difficult to deploy in industrial practice due to their massive parameter size and the substantial computational resources they consume, this research proposes a lightweight human pose estimation algorithm model. The model employs a coordinate-based classification approach and utilizes EfficientNetV2 as the backbone structure of a lightweight neural network for feature extraction from input images. To enhance pose estimation accuracy, this study introduces gated attention units to effectively explore spatial features of key points. Additionally, a hybrid consistency cross-entropy is proposed, which combines the model\'s current round prediction results with manually annotated data to serve as the true probability distribution of the current round sample data, mitigating the negative impact of manual annotation data errors on the model. The proposed model in this paper achieves a performance of 71.7 AP on the COCO dataset, surpassing many similar lightweight models.

Keywords： Computer Science and Technology Human Pose Estimation Deep Learning