An Intelligent Human Pose Detection Comparing with Random forest and Recurrent Neural Network

This research study intends to compare the effectiveness of Random Forest and Recurrent Neural Network (RNN) models in order to construct an intelligent human posture identification system for real-time applications. This entails gathering a variety of annotated human pose datasets, identifying pertinent characteristics, and applying Random Forest and RNN models. The goals of training and optimization are to improve speed, accuracy, and resource efficiency. Metrics including accuracy, precision, recall, and F1 score will be used to assess the models’ performance, with an emphasis on how well they apply in real-time. We will compare the Random Forest and RNN models in detail, taking into account their advantages and disadvantages in various settings. The ultimate goal comprises putting the selected model into practice in a real-time setting, optimizing it in response to performance feedback, and optimized for deployment in environments with limited resources.