Vision-Based Human Activity Recognition Using CNN and LSTM Architecture

Technology’s growing use has facilitated the quality of living. Artificial Intelligence (AI) is the field that aims to define how human intelligence is mimicked by machines which are programmed to think or behave like humans. Modern approaches and tools for evaluating human behavior have been made possible by modern advancements in the fields of machine learning (ML) and artificial intelligence (AI). Due to its applicability in several industries, comprising of entertainment, security and surveillance, health, and intelligent environments, human activity recognition has gained prominence significantly. Human activity recognition (HAR) using video sensors typically involves analyzing the visual data captured by cameras to classify and identify the actions of individuals. In the following paper, we propose ConvLSTM and LRCN-based Human Action Recognition. A huge variety of films from the publicly accessible data set, UCF50 comprises a wide range of activity classes that are used to build a statistical model. For the model proposed in this paper, the accuracy has turned out to be 94%, the average f1-score is 0.93 and the average recall is calculated to be 0.925. The Loss curve has also been plotted along with the accuracy curve for the proposed model for recognizing human activities.