Advanced System for Enhancing Location Identification through Human Pose and Object Detection

dc.contributor.authorMedrano, Kevin
dc.contributor.authorCrespo, Jonathan
dc.contributor.authorGomez, Javier
dc.contributor.authorAlfaro, Cesar
dc.date.accessioned2024-12-19T12:51:21Z
dc.date.available2024-12-19T12:51:21Z
dc.date.issued2023-08-18
dc.description.abstractLocation identification is a fundamental aspect of advanced mobile robot navigation systems, as it enables establishing meaningful connections between objects, spaces, and actions. Understanding human actions and accurately recognizing their corresponding poses play pivotal roles in this context. In this paper, we present an observation-based approach that seamlessly integrates object detection algorithms, human pose detection, and machine learning techniques to effectively learn and recognize human actions in household settings. Our method entails training machine learning models to identify the common actions, utilizing a dataset derived from the interaction between human pose and object detection. To validate our approach, we assess its effectiveness using a diverse dataset encompassing typical household actions. The results demonstrate a significant improvement over existing techniques, with our method achieving an accuracy of over 95% in classifying eight different actions within household environments.. Furthermore, we ascertain the robustness of our approach through rigorous testing in real-world environments, demonstrating its ability to perform well despite the various challenges of data collection in such settings. The implications of our method for robotic applications are significant, as a comprehensive understanding of human actions is essential for tasks such as semantic navigation. Moreover, our findings unveil promising opportunities for future research, as our approach can be extended to learn and recognize a wide range of other human actions. This perspective, which highlights the potential leverage of these techniques, provides an encouraging path for future investigations in this field.
dc.identifier.citationKevin, M.A.; Crespo, J.; Gomez, J.; Alfaro, C. Advanced System for Enhancing Location Identification through Human Pose and Object Detection. Machines 2023, 11, 843. doi: 10.3390/machines11080843
dc.identifier.doi10.3390/machines11080843
dc.identifier.issn2075-1702
dc.identifier.urihttps://hdl.handle.net/10115/44357
dc.language.isoen
dc.publisherMDPI
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectcomputer vision
dc.subjectsemantic navigation
dc.subjectmachine learning
dc.subjecthuman pose
dc.subjectobject detector
dc.subjectalgorithms
dc.titleAdvanced System for Enhancing Location Identification through Human Pose and Object Detection
dc.typeArticle

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