OBJECT RECOGNITION FOR AUGMENTED REALITY APPLICATIONS

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Volume 4 (1), June 2021, Pages 15-28

Vladislav Li¹, Georgios Amponis², Jean-Christophe Nebel¹, Vasileios Argyriou¹, Thomas Lagkas²  and Panagiotis Sarigiannidis³

¹ Department of Networks and Digital Media, Kingston University, London, UK

² Department of Computer Science, International Hellenic University, Greece

³ Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, Greece, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Developments in the field of neural networks, deep learning, and increases in computing systems’ capacity have allowed for a significant performance boost in scene semantic information extraction algorithms and their respective mechanisms. The work presented in this paper investigates the performance of various object classification- recognition frameworks and proposes a novel framework, which incorporates Super-Resolution as a preprocessing method, along with YOLO/Retina as the deep neural network component. The resulting scene analysis framework was fine-tuned and benchmarked using the COCO dataset, with the results being encouraging. The presented framework can potentially be utilized, not only in still image recognition scenarios but also in video processing.

Keywords:

Object Recognition, Scene Analysis, Super Resolution, Machine Learning, High-Performance Computing, Feature Extraction.

DOI: https://doi.org/10.32010/26166127.2021.4.1.15.28

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