Static and dynamic human activity recognition with VGG-16 pre-trained CNN model

Abstract

Human Activity Recognition has been widely studied using the Convolutional Neural Network (CNN) algorithm to classify a person's movements by utilizing data from devices that record movements such as cameras. The benefits generated by this technology are useful for modern devices such as Virtual Reality and Smart Home technology with CCTV cameras. The VGG-16 (Visual Geometric Group with 16 Layers) pre-trained model is one of the models used for transfer learning and has won the Image Net competition. In this study, the authors tested the performance of the VGG-16 model to identify two types of human activity, namely Static and Dynamic. This study uses 1,680 public datasets which are divided into 80% Data Train, 10% Data Validation, and 10% Data Test I. In addition, there are also 100 local datasets as Data Test II. There is no overfitting issue in the training and testing process. The accuracy of the Testing process with public and local images dataset produces a high accuracy of 98.8% and 97% respectively.