Efficient I3D-VGG19-based architecture for human activity recognition

Abstract

Systems for autonomously identifying and analyzing human activities (HAR) make use of video data captured from various devices area requires ongoing updates due to the developing technology and multidisciplinary nature of HAR. Objective of this research is to classify different activity performed by Human using various pretrained models and latest transfer learning methods. Set the hyperparameters values to get accurate classification based on different performance evaluation matrices. In this study, the VGG19 based optimized I3D architecture is proposed. The experimental findings demonstrate that use of optimized VGG19 based I3D model on the UCF-50 dataset has led to an enhancement in the performance of the Human Activity recognition system with accuracy rate of training is 98.24% and testing is 98.36%, surpassing the performance of alternative I3D model using DenseNet121 in direct comparison. This will facilitate the development of applications like Smart Environments, Elderly Care and Assistive Technologies, Healthcare and Wellness and various other domains.