Object Detection with Augmented Reality

Title

Object Detection with Augmented Reality

Creator

Tyagi A.; Rajpal D.; David A.; Singh J.; Thakur H.K.; Upreti K.

Description

This study describes an artificial intelligence (AI)-based object identification system for detecting real-world items and superimposing digital information in Augmented Reality (AR) settings. The system evaluates the camera stream from an AR device for real-Time recognition using deep learning algorithms trained on a collection of real-world items and their related digital information. Object recognition applications in AR include gaming, education, and marketing, which provide immersive experiences, interactive learning, and better product presentations, respectively. However, challenges such as acquiring larger and more diverse datasets, developing robust deep learning algorithms for varying conditions, and optimizing performance on resource-constrained devices remain. The AI-based object recognition system demonstrates the potential to transform AR experiences across domains, while emphasizing the need for ongoing research and development to fully realize its capabilities. 2023 IEEE.

Source

OCIT 2023 - 21st International Conference on Information Technology, Proceedings, pp. 162-167.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Augmented Reality; Deep Learning; Neural Networks; Object Recognition; Visualization

Coverage

Tyagi A., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Rajpal D., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; David A., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Singh J., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Thakur H.K., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Upreti K., Christ University, Department of Computer Science, Ghaziabad, India

Rights

Restricted Access

Relation

ISBN: 979-835035823-0

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/OCIT59427.2023.10430922

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186714970&doi=10.1109%2fOCIT59427.2023.10430922&partnerID=40&md5=885d19fb28657cf8a20b8e4f78d6faf3