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A survey on intelligent human action recognition techniques

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Abstract

Human Action Recognition is an essential research area in computer vision due to its automated nature of video monitoring. Human Action Recognition has several applications, including robotics, video monitoring, health care, elderly monitoring, crowd behavior and the detection of aberrant activity. This study seeks to offer the reader an up-to-date overview of intelligent human activity recognition literature and current advancements in this area. This work discusses the recent state-of-the-art research for activity recognition techniques and challenges associated with identifying human activity and discusses publicly available datasets. This work consists of an in-depth survey of numerous works published from 2010 to 2022 focusing on intelligent techniques. This article describes all steps of human action recognition along with their techniques. This study comes with the Datasets for Human Action Recognition, Handcrafted-Feature technique, Machine Learning (ML), Deep-Learning (DL), Hybrid Deep Learning and limitation of this area. This study offers a comparative analysis between ML and DL approaches to show their effectiveness in action recognition. This study examines some unexplored areas in human action recognition that can be unearthed to create a more resilient system in the presence of issues. Previous research has demonstrated that deep learning surpasses standard machine learning for recognizing human activities. This study also emphasizes the most pressing issues and research direction. All relevant datasets are described in detail. Furthermore, our opinions and suggestions for future research have been shared. Compared to past surveys, this study offers a more systematic description of Human Action Recognition methods regarding comparability, problems, and the most recent evaluation technique.

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    Rahul Kumar & Shailender Kumar

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Kumar, R., Kumar, S. A survey on intelligent human action recognition techniques. Multimed Tools Appl 83, 52653–52709 (2024). https://doi.org/10.1007/s11042-023-17529-6

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