preprints.org > computer science and mathematics > computer vision and graphics > doi: 10.20944/preprints202310.0200.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 30 September 2023 / Approved: 2 October 2023 / Online: 4 October 2023 (08:03:43 CEST)

A major challenge of augmented and mixed reality applications is identifying the context and semantics of the real environment. Studies on object and action recognition were developed based on the improvement of machine learning techniques, allowing them to be annotated and recognized. This study aims to characterize current knowledge on the use of machine learning for recognizing objects and actions in augmented and mixed reality environments, increasing context awareness. Therefore, a systematic literature review of works related to these topics was made, using the Scopus and Web of Science knowledge bases. We searched articles and conference reviews or papers published between 2018 and 2022 and selected fifteen studies to be reviewed. The results indicate that there is a great demand for using machine learning to immersive technologies in factories, engineering, entertainment, education, health, among other application domains. However, these real-time interactive systems still have challenges and limitations to be solved, involving network communication, prediction time and the creation of a model that recognize objects and actions in broad contexts. Furthermore, additional research is needed to investigate how object and action recognition can increase context awareness in augmented reality applications.

machine learning; augmented reality; mixed reality; object recognition; action recognition; context analysis

Computer Science and Mathematics, Computer Vision and Graphics

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