Human Activity Recognition Using Deep Learning : A Survey

Abstract

With the use of deep learning algorithms from artificial intelligence (AI), several types of research have been conducted on video data. Object localization, behaviour analysis, scene understanding, scene labelling, human activity recognition (HAR), and event recognition make up the majority of them. Among all of them, HAR is one of the most difficult jobs and key areas of research in video data processing. HAR can be used in a variety of fields, including robotics, human-computer interaction, video surveillance, and human behaviour categorization. This research seeks to compare deep learning approaches on several benchmark video datasets for vision-based human activity detection. We suggest a brand-new taxonomy for dividing up the literature into CNN- and RNN-based methods. We further categorise these approaches into four subgroups and show several methodologies, their effectiveness, and experimental datasets. To illustrate the development of HAR techniques, a brief comparison is also provided with the handcrafted feature-based approach and its merger with deep learning. Finally, we go over potential future research areas and some unresolved issues with recognising human activities. This survey's goal is to present the most recent developments in HAR techniques for vision-based deep learning using the most recent literature analysis.