Augmented Reality Frameworks for Object Recognition in Learning Application Domain: A Systematic Review

Abstract

Object recognition is a technology based on computer vision techniques to identify the object in images or video. A previous study revealed that object recognition is useful to improve the learning outcome in augmented reality applications. However, there is yet to be a comprehensive augmented reality (AR) framework on object recognition in the learning application domain setting. This study aims to review the existing AR frameworks in object recognition and highlight the important components needed in AR framework. This Systematic Literature Review (SLR) was developed based on the PRISMA method. A preliminary search resulted in a total of 70 articles. After the removal of duplicates and a more rigorous screening based on the titles and abstracts, the number of articles was reduced to 26 articles. Then, for final review, only 9 articles were chosen. Based on the SLR, the components needed in the AR framework for object recognition are tracking, image classification, object localization, object detection, object segmentation, interface and interaction. Some of the limitations identified from the existing AR framework for object recognition include the lower-resolution camera that uses compressed images and due to the low detection accuracy and the limited of the tools. The proposed framework suggests the initial for AR framework object recognition.