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Title
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Deep learning model compression for resource efficient activity recognition on edge devices : a case study
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Author
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Abstract
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| This paper presents an approach to adapt an existing activity recognition model for efficient deployment on edge devices. The used model, called YOWO (You Only Watch Once), is a prominent deep learning model. Given its computational complexity, direct deployment on resource-constrained edge devices is challenging. To address this, we propose a two-stage compression methodology consisting of structured channel pruning and quantization. The goal is to significantly reduce the model’s size and computational needs while maintaining acceptable task performance. Our experimental results, obtained by deploying the compressed model on Raspberry Pi 4 Model B, confirm that our approach effectively reduces the model’s size and operations while maintaining satisfactory performance. This study paves the way for efficient activity recognition on edge devices. |
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Language
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English
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Source (book)
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Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 27-29 February, 2024, Rome, Italy
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Publication
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2024
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ISBN
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978-989-758-679-8
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DOI
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10.5220/0012423300003660
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Volume/pages
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p. 575-584
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Full text (Publisher's DOI)
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Full text (open access)
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