A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications

Li, V., Villarini, B., Nebel, JC. and Argyriou, V. 2023. A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications. The IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology. Bali, Indonesia 13 - 15 Jul 2023 IEEE . https://doi.org/10.1109/IAICT59002.2023.10205667

TitleA Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications
AuthorsLi, V., Villarini, B., Nebel, JC. and Argyriou, V.
TypeConference paper
Abstract

Taking as input natural images and videos augmented reality (AR) applications aim to enhance the real world with superimposed digital contents enabling interaction between the user and the environment. One important step in this process is automatic scene analysis and understanding that should be performed both in real time and with a good level of object recognition accuracy. In this work an end-to-end framework based on the combination of a Super Resolution network with a detection and recognition deep network has been proposed to increase performance and lower processing time. This novel approach has been evaluated on two different datasets: the popular COCO dataset whose real images are used for benchmarking many different computer vision tasks, and a generated dataset with synthetic images recreating a variety of environmental, lighting and acquisition conditions. The evaluation analysis is focused on small objects, which are more challenging to be correctly detected and recognised. The results show that the Average Precision is higher for smaller and low resolution objects for the proposed end-to-end approach in most of the selected conditions.

KeywordsAugmented Reality
Object Detection
Scene Analysis
Scene Understanding
Object Recognition
Deep Learning
Super-Resolution
Feature Extraction
Year2023
ConferenceThe IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology
PublisherIEEE
Accepted author manuscript
File Access Level
Open (open metadata and files)
Publication dates
Published13 Jul 2023
JournalIAICT 2023 Conference Proceedings
Digital Object Identifier (DOI)https://doi.org/10.1109/IAICT59002.2023.10205667

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