Enhancing Smart Campus Monitoring: YOLOv8-Based Activity Recognition in Ethiopian Higher Education

Enhancing Smart Campus Monitoring: YOLOv8-Based Activity Recognition in Ethiopian Higher Education | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 September 2025 V1 Latest version Share on Enhancing Smart Campus Monitoring: YOLOv8-Based Activity Recognition in Ethiopian Higher Education Authors : Degale Abe 0009-0007-6505-3970 [email protected] , Tamirat Tumoro , and Jian Cheng Authors Info & Affiliations https://doi.org/10.22541/au.175791437.71956351/v1 327 views 148 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid expansion and application of computer vision which demands the installation of surveillance cameras for sophisticated smart classes. This paper describes a real-time activity recognition framework based on deep learning to enhance monitoring and educational quality in higher education. The proposed paper applied using YOLOv8 based model, the state-of-the-art in action recognition, to detect abnormal activities and unauthorized intrusions in higher education. Nevertheless, the implementation of such systems in resource-limited environment, such as Ethiopian higher education institutions, poses unique challenges related to smart class resources and data scarcity. This study focuses in Ethiopian higher education by introducing EthioCAD, a novel dataset to classify academic activities recognition across smart classroom teaching learning process. The dataset was constructed by addressing challenges in gathering real-world scenarios, employing the methodology of AI driven RoboFlow framework for frame extraction, annotation, and dataset organization. The novel EthioCAD dataset contains 4,224 KB of extracted frames and 33,485 MB of images. Where the EthioCAD dataset, available at https://github.com/Degale-Desta/EthioCAD/, serves as a valuable resource for advancing research in academic activity. To train dataset and evaluate its performance, we implemented the YOLOv8 model in high performing power of GPU in Google Colab resource where the accuracy reaching 90.2% and 78.6% on mAP50 and mAP50-95, respectively while these results maintaining high accuracy, ensuring proactive campus safety. Correspondingly results highlight the effectiveness of YOLOv8 for ensuring of safety and quality in teaching learning activity with auto-detected threats and real-time alerts which also a solid foundation for future studies in smart class based recognition. Supplementary Material File (september campus_activity_detection_modified.docx) Download 4.04 MB Information & Authors Information Version history V1 Version 1 15 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computer vision education image classification machine learning Authors Affiliations Degale Abe 0009-0007-6505-3970 [email protected] University of Electronic Science and Technology of China View all articles by this author Tamirat Tumoro Wachemo University View all articles by this author Jian Cheng University of Electronic Science and Technology of China View all articles by this author Metrics & Citations Metrics Article Usage 327 views 148 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Degale Abe, Tamirat Tumoro, Jian Cheng. Enhancing Smart Campus Monitoring: YOLOv8-Based Activity Recognition in Ethiopian Higher Education. Authorea . 15 September 2025. DOI: https://doi.org/10.22541/au.175791437.71956351/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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