Exploration of Markers in Augmented Reality: Methodologies and Applications
Sanchit Vashisht · 2024
Augmented Reality (AR) is a cutting-edge technology that combines digital content with the real environment, resulting in improved user experiences across several fields. Marker identification algorithms are crucial in augmented reality (AR) applications since they enable precise placement of virtual objects onto the actual world. This study investigates a wide range of techniques for identifying markers, including both conventional image processing algorithms and cutting-edge machine learning models. This study employs comparative analysis to precisely define the strengths and limitations of each technique, providing significant insights into their suitability in various scenarios. The research analyses three main methodologies: template matching, feature detection, and deep learning-based techniques. It offers insights into their individual performance measures, including accuracy, speed, robustness, and scalability. Empirical evidence and research substantiate the efficacy of these methods in several fields, such as gaming, education, healthcare, and manufacturing. The research showcases significant progress while simultaneously highlighting unique constraints, such as the limited availability of various datasets, processing limitations, and the difficulties presented by dynamic augmented reality (AR) environments. In future research, the focus will be on improving current strategies to overcome these limits and investigating hybrid approaches that combine the characteristics of multiple methodologies. The combination of interdisciplinary collaboration and technological breakthroughs such as edge computing and 5G networks has the potential to improve the efficiency and feasibility of marker identification systems in augmented reality (AR). This research finally enhances the comprehension and utilization of marker identification in augmented reality (AR), hence facilitating inventive encounters and imaginative applications in diverse domains.