Recognition of Human Activity Using Deep Learning Models

DOI

10.2991/978-94-6239-616-6_12How to use a DOI?

Keywords

CCTV surveillance; multi-activity detection; intelligent monitoring; computer vision; deep learning; resource tracking; real-time analytics; context-aware detection; automation; intelligent video analysis; domain-specific detection

Abstract

Human Activity Recognition aims to automatically identify human actions using video, wearable sensors, or environmental data. It is widely used in hospitals, schools, public areas, malls, and smart environments for safety monitoring, anomaly detection, behavior understanding, and automation. Traditional HAR systems mainly relied on handcrafted features, which made them sensitive to real-world challenges such as lighting changes, occlusions, background variations, and complex human movements. Because of this, their performance often dropped in uncontrolled settings. With the rise of deep learning, HAR has become more accurate and robust. Modern models such as CNNs, RNNs, 3D-CNNs, GCNs, and transformers can automatically extract spatial temporal features, understand motion patterns, and handle multi-actor scenarios more effectively. This paper is a survey that reviews recent deep learning-based HAR methods. Instead of introducing a new model, we provide an organized overview of existing techniques, discuss their strengths and weaknesses, and highlight important research trends and open challenges. The goal is to guide future work and support the development of more efficient HAR systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. Ranjeeth
AU  - P. Yogapriya
AU  - S. Keerthi
AU  - N. Sengeniammal
PY  - 2026
DA  - 2026/03/31
TI  - Recognition of Human Activity Using Deep Learning Models
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 145
EP  - 151
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_12
DO  - 10.2991/978-94-6239-616-6_12
ID  - Ranjeeth2026
ER  -