Exploring Image Recognition with Deep Learning in PoseNet

Abstract

This study aims to explore the potential and efficiency of deep learning techniques in applying PoseNet for image recognition. The focus is on improving the accuracy and stability of PoseNet in recognizing human poses in complex environments and expanding its applications across different industries. The research includes discussions on the applications of PoseNet in health monitoring and sports training, as well as the challenges and limitations. The results indicate that combining deep learning and computer vision techniques can significantly enhance the performance of PoseNet in image recognition, especially in complex environments. Future research is suggested to explore more advanced neural network models and data processing techniques further to improve the model's accuracy and adaptability. Additionally, it is recommended that other sensing technologies be integrated into practical applications to enhance the overall performance and reliability of the system.