International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Adaptive Testing For Real-Time Object Recognition in AR Applications

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Volume: 4 | Issue: 5 | Pages: 1139-1144

Subject: Engineering

Country: United States

DOI: https://doi.org/10.54660/IJMRGE.2023.4.5.1139-1144

License: CC BY 4.0

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Abstract

3D Object recognition is one of the most important factors behind Augmented Reality (AR) applications to provide immersive and interactive experiences. They optimize recognition accuracy and responsiveness despite varying lighting conditions, motion blur, occlusions, and time constraints. We propose a viewing synthesis and performance training framework for adaptive AR object detection. They would generate the tests, and as per simulated and real-world contexts, they would check the smooth recognition in action-oriented dynamic environments. Performance optimization may encompass model compression, edge computing, adaptive learning, hardware acceleration and energy-aware computing to maximize real-time processing with retained accuracy. The study highlights how AR applications can leverage AI-enabled adaptive learning and self-optimizing. The advancements in federated learning, multi-sensor fusion and 5G illuminating AR frameworks will usher in seamless navigation experiences across various fields (education, retail, geolocation) and enable real-time object recognition capability.

How to Cite This Article

Santosh Kumar Jawalkar (2023). Adaptive Testing For Real-Time Object Recognition in AR Applications . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 4(5), 1139-1144. DOI: https://doi.org/10.54660/IJMRGE.2023.4.5.1139-1144

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Publication Information

Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

Frequency: Bimonthly

Language: English

Open Access: Yes - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

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