Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/14715
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dc.contributor.authorKarandikar, Mihir Kanchanen_US
dc.contributor.authorJain, Ankiten_US
dc.contributor.authorShrivastava, Abhisheken_US
dc.date.accessioned2024-10-25T05:50:58Z-
dc.date.available2024-10-25T05:50:58Z-
dc.date.issued2024-
dc.identifier.citationKarandikar, M., Jain, A., & Srivastava, A. (2024). Lightweight human activity recognition system for resource constrained environments. Journal of Electronic Imaging. Scopus. https://doi.org/10.1117/1.JEI.33.4.043025en_US
dc.identifier.issn1017-9909-
dc.identifier.otherEID(2-s2.0-85203253893)-
dc.identifier.urihttps://doi.org/10.1117/1.JEI.33.4.043025-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/14715-
dc.description.abstractAs the elderly population in need of assisted living arrangements continues to grow, the imperative to ensure their safety is paramount. Though effective, traditional surveillance methods, notably RGB cameras, raise significant privacy concerns. This paper highlights the advantages of a surveillance system addressing these issues by utilizing skeleton joint sequences extracted from depth data. The focus on non-intrusive parameters aims to mitigate ethical and privacy concerns. Moreover, the proposed work prioritizes resource efficiency, acknowledging the often limited computing resources in assisted living environments. We strive for a method that can run efficiently even in the most resource-constrained environments. Performance evaluation and a prototypical implementation of our method on a resource-constraint device confirm the efficacy and suitability of the proposed method in real-world applications. © 2024 SPIE and IS&T.en_US
dc.language.isoenen_US
dc.publisherSPIEen_US
dc.sourceJournal of Electronic Imagingen_US
dc.subjectconstraint environmenten_US
dc.subjectensemble learningen_US
dc.subjecthuman activity recognitionen_US
dc.subjectprivacy-preservingen_US
dc.subjectskeletal representationen_US
dc.titleLightweight human activity recognition system for resource constrained environmentsen_US
dc.typeJournal Articleen_US
Appears in Collections:Department of Computer Science and Engineering

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