Augmented-Reality Survey: from Concept to Application

doi:10.3837/tiis.2017.02.019

Cited by 18 publications

(7 citation statements)

References 63 publications

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“…Some evaluations have solely taken journal articles into account, ignoring conference papers and other documents. Conversely, some reviews have focused on the gap between conventional teaching methods (Wu et al, 2013), tools and technologies, benefits and limitations (Kim et al, 2017) and so on. Thus, the synthesis and conclusions associated with research on this topic are complicated by these limitations.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric mapping of theme and trends of augmented reality in the field of education

Harnal,

Sharma,

Anupriya

et al. 2023

Computer Assisted Learning

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BackgroundAn innovative and interactive real‐world environment can be presented with augmented reality (AR) that comprises digital visual elements, audio, or other sensory information delivered via technology to enhance one's experience. AR has numerous potential applications in various everyday fields. The education sector is one such arena where AR has been implemented prominently globally. AR in education can impact active students' learning to memorize effectively and can persuade them to engage in a meaningful process of learning.ObjectivesThis article presents a bibliometric analysis of some recent applications of AR, emphasizing the potential effects, challenges, and trends of AR in the education sector for the past 15 years.MethodsA total of 3909 documents published during 2006–2020 were retrieved from the Scopus database for analysis. The bibliometric analysis included document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of the most cited documents. Additionally, it provides a brief account of the challenges for AR adaptation in education.Results and conclusionsThe findings uncover an increased use of AR to promote enhanced learning in the education field. This work will provide the survey, limitations, and drawbacks, as well as open usability and technical issues that may represent new challenges for future research in this field.

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Section: Introductionmentioning

confidence: 99%

Bibliometric mapping of theme and trends of augmented reality in the field of education

Harnal,

Sharma,

Anupriya

et al. 2023

Computer Assisted Learning

View full text Add to dashboard Cite

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“…Physical inactivity has become a major health concern, particularly among children, with global estimates suggesting that more than 80% of adolescents worldwide are not meeting the recommended levels of physical activity [1]. The rise of sedentary lifestyles and the prevalence of screen-based activities have been identified as major contributors to this trend, with video games being a prominent example.…”

Section: Introductionmentioning

confidence: 99%

Applying Artificial Intelligence and Computer Vision for Augmented Reality Game Development in Sports

Omarov,

Baibaktina

et al. 2023

IJACSA

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This paper delineates the intricate process of crafting an Augmented Reality (AR)-enriched version of the Subway Surfers game, engineered with an emphasis on action recognition and the leverage of Artificial Intelligence (AI) principles, with the primary objective of boosting children's enthusiasm towards physical activity. The gameplay, fundamentally predicated on advanced computer vision methodologies for discerning player kinesthetics, and reinforced with machine learning tactics for modulating the intricacy of the game in accordance with player capabilities, offers an immersive and engaging interface. This innovative amalgamation serves to not only catalyze children's interest in participating in active exercises, but also introduces a playful aspect to it. The procedural development of the game required the cohesive assimilation of a diverse spectrum of technologies, encompassing Unity for game development, TensorFlow for implementing machine learning algorithms, and Vuforia for crafting the AR elements. A preliminary study, conducted to assess the efficacy of the game in fostering a pro-sport attitude in children, reported encouraging outcomes. Given the potential of the game to incite physical activity among young users, it could be construed as a promising antidote to sedentarism and a potent catalyst for endorsing a healthier lifestyle.

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“…Mobile robots are capable of determining their own motion trajectories in uncharted territory utilizing simultaneous localization and mapping (SLAM) [1,2], which enables the generation of maps of their surroundings. The application of SLAM technology is widespread in industries, including mobile robots, virtual reality [3,4], smart mobile homes, and autonomous driving [5]. Visual sensors are accessible and can capture detailed images; thus, visual SLAM with cameras has broad appeal [6].…”

Section: Introductionmentioning

confidence: 99%

Loop Closure Detection Method Based on Similarity Differences between Image Blocks

Huang,

Zhang

et al. 2023

Sensors

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Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map’s general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate’s accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality.

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Augmented-Reality Survey: from Concept to Application

Cited by 18 publications

References 63 publications

Bibliometric mapping of theme and trends of augmented reality in the field of education

Bibliometric mapping of theme and trends of augmented reality in the field of education

Applying Artificial Intelligence and Computer Vision for Augmented Reality Game Development in Sports

Loop Closure Detection Method Based on Similarity Differences between Image Blocks

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