A survey of machine learning in pedestrian localization systems: applications, open issues and challenges

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dc.contributor.authorMirama Pérez, Víctor Fabián
dc.contributor.authorDíez Blanco, Luis Enrique
dc.contributor.authorBahillo, Alfonso
dc.contributor.authorQuintero, Víctor
dc.date.accessioned2025-09-03T10:48:53Z
dc.date.available2025-09-03T10:48:53Z
dc.date.issued2021-08-26
dc.date.updated2025-09-03T10:48:52Z
dc.description.abstractWith the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.en
dc.identifier.citationMirama, V. F., Diez, L. E., Bahillo, A., & Quintero, V. (2021). A survey of machine learning in pedestrian localization systems: applications, open issues and challenges. IEEE Access, 9, 120138-120157. https://doi.org/10.1109/ACCESS.2021.3108073
dc.identifier.doi10.1109/ACCESS.2021.3108073
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3474
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherLocalization system taxonomy
dc.subject.otherMachine learning
dc.subject.otherMachine learning taxonomy
dc.subject.otherPedestrian localization systems
dc.subject.otherReinforcement learning
dc.subject.otherScene analysis
dc.subject.otherSupervised learning
dc.subject.otherUnsupervised learning
dc.titleA survey of machine learning in pedestrian localization systems: applications, open issues and challengesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage120157
oaire.citation.startPage120138
oaire.citation.titleIEEE Access
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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