Publication
Title
Deep learning model compression for resource efficient activity recognition on edge devices : a case study
Author
Abstract
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.
Language
English
Source (book)
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 27-29 February, 2024, Rome, Italy
Publication
2024
ISBN
978-989-758-679-8
DOI
10.5220/0012423300003660
Volume/pages
p. 575-584
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
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Record
Identifier
Creation 22.03.2024
Last edited 23.03.2024
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