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

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Title	A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications
Authors	Li, V., Villarini, B., Nebel, JC. and Argyriou, V.
Type	Conference 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.
Keywords	Augmented Reality
	Object Detection
	Scene Analysis
	Scene Understanding
	Object Recognition
	Deep Learning
	Super-Resolution
	Feature Extraction
Year	2023
Conference	The IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology
Publisher	IEEE
Accepted author manuscript	IEEE_IAICTPaper_SceneUnderstanding.pdf File Access Level Open (open metadata and files)
Published	13 Jul 2023
Journal	IAICT 2023 Conference Proceedings
Digital Object Identifier (DOI)	https://doi.org/10.1109/IAICT59002.2023.10205667

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

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