Comparative performance analysis of deep neural networks for human activity recognition

Abstract

Human activity recognition is a challenging computer vision problem, which involves identifying human activities using artificial intelligence methods, from the data collected from various sensors. In this paper, existing deep neural networks are studied and a custom deep architecture is proposed for human pose and activity recognition. The proposed architecture is mainly based on the approach of localizing human body joints in a 3D space on a single 2D image. The fundamental problem is the wide variations in appearance, caused by various camera angles which include, clothing, body shapes, and self-occlusion, and complications in background variations. The Human 3.6M and patient MoCap datasets are used as a benchmark and our proposed architecture shows competitive performance compared to the state-of-the-art methods.

Document Type

Conference Proceeding

Date of Publication

1-1-2023

School

School of Engineering

Copyright

subscription content

Publisher

IEEE

Comments

Burney, M. A., Waheed, A., Khan, S., Ghori, S., Shaikh, M. B., & Qureshi, R. (2023, November). Comparative performance analysis of deep neural networks for human activity recognition [Paper presentation]. 2023 18th International Conference on Emerging Technologies (ICET), Peshawar, Pakistan. https://doi.org/10.1109/ICET59753.2023.10374609

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Link to publisher version (DOI)

10.1109/ICET59753.2023.10374609