Comparative performance analysis of deep neural networks for human activity recognition
Document Type
Conference Proceeding
Publication Title
2023 18th International Conference on Emerging Technologies (ICET)
First Page
62
Last Page
67
Publisher
IEEE
School
School of Engineering
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.
DOI
10.1109/ICET59753.2023.10374609
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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