scholarly journals Pengenalan alat musik tradisional Bangka dengan Marker-Based Augmented Reality

2019 ◽  
Vol 5 (2) ◽  
pp. 89 ◽  
Author(s):  
Fransiskus Panca Juniawan ◽  
Dwi Yuny Sylfania ◽  
Harrizki Arie Pradana ◽  
Laurentinus Laurentinus
Keyword(s):  
Augmented Reality ◽  
Light Intensity ◽  
Object Detection ◽  
Real Time ◽  
Performance Test ◽  
3D Models ◽  
Musical Instruments ◽  
Paper Test ◽  
Strongly Agree ◽  
High Level

Dengan berkembangnya teknologi, kesadaran akan pentingnya alat musik tradisional menjadi berkurang. Demikian juga dengan alat musik tradisional Bangka yang mulai kehilangan popularitasnya. Kondisi saat ini, para remaja di Bangka kebanyakan tidak dapat memainkan alat musik tradisionalnya. Begitu juga dengan anak-anak yang belum mengetahui dan bahkan tidak mengenal alat musik tradisional daerah mereka. Jika kondisi ini dibiarkan, dikhawatirkan keberadaan alat musik tradisional Bangka akan hilang, begitu juga dengan sumber daya manusia yang dapat memainkannya. Untuk menghindari hal tersebut, dibuatlah aplikasi pengenalan alat musik tradisional Bangka menggunakan Augmented Reality (AR). AR dipilih karena dapat memberikan gambaran alat musik secara real time dalam bentuk 3D sesuai dengan pergerakan kamera smartphone yang dinamis. Empat objek 3D alat musik yakni dambus, rebab, rebanatamborin, dan gong yang dibuat menggunakan Autodesk Maya. AR yang dibangun menggunakan metode berbasis marker. Metode ini dipilih agar lebih mudah digunakan oleh pengguna yang mayoritasnya adalah anak-anak. Selain itu, kelebihan metode ini memiliki tingkat akurasi posisi yang sangat tinggi. Unity sebagai engine untuk penerapan AR 3D modelling pada sistem Android dan Vuforia SDK sebagai engine pembentuk marker augmented reality. Pengujian fungsional memiliki hasil 100% dengan sistem yang berjalan baik. Hasil pengujian kinerja deteksi objek AR berdasarkan intensitas cahaya diketahui bahwa smartphone yang memiliki dua kamera di bagian belakang dapat mendeteksi objek dengan intensitas cahaya 0 Lux pada malam hari dengan kondisi gelap, sedangkan yang hanya memiliki satu kamera tidak dapat mendeteksi objek. Pengujian warna marker mendapatkan hasil modifikasi warna marker pink, kuning, dan hitam yang masih memungkinkan untuk pendeteksian objek, walaupun objek yang tampil tidak stabil. Dari pengujian kertas marker diketahui bahwa jenis kertas tidak berpengaruh terhadap pendeteksian objek. Pengujian beta dilakukan dengan cara membagikan kuesioner terkait pengalaman pengguna dalam penggunaan sistem. Hasil survei diketahui pengguna merasa sangat setuju dengan nilai sebesar 80%, bahwa penggunaan sistem dapat membantu mereka dalam mengenal alat musik tradisional Bangka.   With the incessant development of technology, awareness on the importance of traditional musical instruments has declined. Similarly, teenagers living in Bangka no longer play their traditional musical instruments, and children are not exposed to their cultural heritage. However, if this continues, it is feared that the existence of traditional Bangka musical instruments will soon go extinct. To avoid this, researchers have proposed an application to identify this media using Augmented Reality (AR). This technique was chosen due to its ability to provide visuals of musical instruments in real time using 3D models in accordance with the dynamic movement of smartphone cameras. This comprises of four 3D objects namely dambus, rebab, rebanatamborin, and gong, which were designed and developed using Autodesk Maya. AR is built using marker-based methods, which was chosen for easy use because majority of its users are children, and its high level of accuracy. Unity was utilized as an engine for its implementation in the Android system, and Vuforia SDK as augmented reality marker-builder engine. Functional testing showed 100% results which means that the system is running well. From the results of the AR object detection performance test based on light intensity it is known that a smartphone with two cameras in the backside has the ability to detect objects with a light intensity of 0 Lux in dark rooms, while the other smartphone with one camera failed to detect the objects. Color testing obtained a modification of marker colors comprising of pink, yellow, and black which are still able to detect objects, although not stable. The paper test marker has no effect on object detection. Beta testing questionnaires were used to obtain information related to user experience. From the survey results, it is known that users strongly agree (80%) that the use of the system helps them to recognize traditional Bangka musical instruments.

Geologic Modelling Using Augmented Reality

2021 ◽  
Author(s):  
Phathompat Boonyasaknanon ◽  
Raymond Pols ◽  
Katja Schulze ◽  
Robert Rundle
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
3D Models ◽  
New Approach ◽  
Domain Experts ◽  
3D Objects ◽  
Track Record ◽  
Initial Work ◽  
Geologic Model ◽  
Time Critical

Abstract An augmented reality (AR) system is presented which enhances the real-time collaboration of domain experts involved in the geologic modeling of complex reservoirs. An evaluation of traditional techniques is compared with this new approach. The objective of geologic modeling is to describe the subsurface as accurately and in as much detail as possible given the available data. This is necessarily an iterative process since as new wells are drilled more data becomes available which either validates current assumptions or forces a re-evaluation of the model. As the speed of reservoir development increases there is a need for expeditious updates of the subsurface model as working with an outdated model can lead to costly mistakes. Common practice is for a geologist to maintain the geologic model while working closely with other domain experts who are frequently not co-located with the geologist. Time-critical analysis can be hampered by the fact that reservoirs, which are inherently 3D objects, are traditionally viewed with 2D screens. The system presented here allows the geologic model to be rendered as a hologram in multiple locations to allow domain experts to collaborate and analyze the reservoir in real-time. Collaboration on 3D models has not changed significantly in a generation. For co-located personnel the approach is to gather around a 2D screen. For remote personnel the approach has been sharing a model through a 2D screen along with video chat. These approaches are not optimal for many reasons. Over the years various attempts have been tried to enhance the collaboration experience and have all fallen short. In particular virtual reality (VR) has been seen as a solution to this problem. However, we have found that augmented reality (AR) is a much better solution for many subtle reasons which are explored in the paper. AR has already acquired an impressive track record in various industries. AR will have applications in nearly all industries. For various historical reasons, the uptake for AR is much faster in some industries than others. It is too early to tell whether the use of augmented reality in geological applications will be transformative, however the results of this initial work are promising.

scholarly journals Real-Time Water Surface Object Detection Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3523 ◽  
Author(s):  
Lili Zhang ◽  
Yi Zhang ◽  
Zhen Zhang ◽  
Jie Shen ◽  
Huibin Wang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Water Surface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
The North ◽  
Aspect Ratios ◽  
High Level ◽  
Surface Object

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.

Occlusion Handling in Augmented Reality Using Background-Foreground Segmentation and Projective Geometry

2005 ◽  
Vol 14 (3) ◽  
pp. 264-277 ◽  
Author(s):  
Hee Lin Wang ◽  
Kuntal Sengupta ◽  
Pankaj Kumar ◽  
Rajeev Sharma
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Structural Model ◽  
Real Life ◽  
3D Models ◽  
Video Stream ◽  
Foreground Segmentation ◽  
Camera Motion ◽  
3D Objects ◽  
Active Research

Developing a seamless merging of real and virtual image streams and 3D models is an active research topic in augmented reality (AR). We propose a method for real-time augmentation of real videos with 2D and 3D objects by addressing the occlusion issue in an unique fashion. For virtual planar objects (such as images), the 2D overlay is automatically overlaid in a planar region selected by the user in the video. The overlay is robust to arbitrary camera motion. Furthermore, a unique background-foreground segmentation algorithm renders this augmented overlay as part of the background if it coincides with foreground objects in the video stream, giving the impression that it is occluded by foreground objects. The proposed technique does not require multiple cameras, camera calibration, use of fiducials, or a structural model of the scene to work. Extending the work further, we propose a novel method of augmentation by using trifocal tensors to augment 3D objects in 3D scenes to similar effect and implement it in real time as a proof of concept. We show several results of the successful working of our algorithm in real-life situations. The technique works on a real-time video from a USB camera, Creative Webcam III, onaPIV1.6GHz system without any special hardware support.

scholarly journals Augmented Reality Application for Laptop Assembly with Assembly Complexity Study

2021 ◽  
Author(s):  
Chiew Jin Hong ◽  
Aun Naa Aun Sung
Keyword(s):  
User Interface ◽  
Speech Recognition ◽  
Augmented Reality ◽  
3D Models ◽  
Assembly Process ◽  
Assembly Sequence ◽  
Augmented Reality Application ◽  
Assembly Instructions ◽  
Guided Assembly ◽  
High Level

Abstract Augmented Reality (AR) in the assembly process will improve the user's experience by providing interactive instructions in real time. However, no previous application of AR guided assembly for laptops with a high level of assembly complexity has been developed. The research aims to develop an AR guided assembly application to provide instruction on the assembly of a laptop. The assembly complexity of the laptop was also investigated. The development of the AR application involves the creation of model target, 3D models and animations, and the development of user interface. The laptop assembly consists of ten steps. Each step comprises animated 3D models and text detailing the assembly instructions. Speech recognition has been used to navigate the assembly sequence. The AR application has successfully been developed for laptop assembly with an assembly complexity of 6.63. With the developed application, the performance of the laptop assembly can be accelerated.

scholarly journals Smart Architectural and Urban Heritage: An Applied Reflection

Heritage ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 2044-2053
Author(s):  
Stefano Brusaporci ◽  
Pamela Maiezza
Keyword(s):  
Augmented Reality ◽  
Multimedia Information ◽  
Real Time ◽  
Case Studies ◽  
Digital Technologies ◽  
3D Models ◽  
Current Configuration ◽  
Urban Heritage ◽  
Mobile Ar

The aim of this paper is to present the use of 3D models and augmented reality (AR) to study and communicate architectural and urban values and, therefore, favor the development of dedicated forms of “smart heritage”. The study rises from a reflection on the concept of “heritage”, as defined in the international documents, intended as an evolving idea that puts together tangible and intangible aspects. Moreover, digital technologies favor “phygital” applications where the digital dimension support the traditional ones. In this way, AR allows the superimposition of multimedia information to heritage, respecting the historical matter of the artefacts, and supporting a “smart heritage” application. In particular, mobile AR, with real-time and ubiquitous visualizations, offers the opportunity to show past urban and architectural configurations to investigate and describe the transformations that have led to the current configuration, and consequently highlighting the present historical and architectural values of the buildings. Two case studies are presented: the square of St. Basilio Monastery, with its historical transformations, and the Basilica of Collemaggio, a pivotal building in the rites of “Perdonanza Celestiniana”.

scholarly journals AR Amenity Perceiver

2020 ◽  
Vol 8 (5) ◽  
pp. 2847-2850
Keyword(s):  
Augmented Reality ◽  
Object Detection ◽  
Real Time ◽  
User Experience ◽  
Mobile Application ◽  
Furniture Industry ◽  
Android Application

The major problem in the Furniture industry is choosing the appropriate furniture for the residence or office. The users are feeling difficult to visualize furniture from a catalog and so changing the furniture textures after purchase would be inconvenient. What: Our solution is a powerful mobile application to render 3D Furniture models into augmented reality. This application features AR experience of the Furniture in reality. The inspiration driving this investigation is to consider and develop an android application called 'AR Amenity Perceiver' with the usage of Augmented Reality advancement for structure and improvement that will help users with envisioning how furniture pieces will look and fit in their homes and besides can give nuances of things to help customer decision. How: The application supports plane and object detection to place and track Furniture in real time. Since the application is built with React Native, it is platform independent and supports real time stutter less object rendering. Why: The client can utilize View in Room 3D mode to envision the furnishings or stylistic theme components in the encompassing space with the assistance of AR. This allows users to check out the Furniture with available texture options and thus making the user experience more realistic before buying

scholarly journals Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5080
Author(s):  
Baohua Qiang ◽  
Ruidong Chen ◽  
Mingliang Zhou ◽  
Yuanchao Pang ◽  
Yijie Zhai ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Image Data ◽  
Semantic Segmentation ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Vital Role ◽  
Multi Scale ◽  
Segmentation Task ◽  
High Level

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

scholarly journals Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality

2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Vivian Alfionita Sutama ◽  
Suryo Adhi Wibowo ◽  
Rissa Rahmania
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Augmented Reality ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Rate ◽  
Batch Size ◽  
Single Shot

Nowadays, Artificial Intelligence is one of the most developing technology, especially on Augmented Reality (AR). AR is a technology which connected between real world and virtual in a real time that allows user to interact directly and display it in 3D. AR technology has two methods, that are AR based on marker and AR based on markerless. However, AR based on marker need an object detection system which has high performance as an interaction tools between user and the device. Single shot multibox detector (SSD) is an object detection algorithm that has fast learning computation and good performance. This method is affected by some parameters like number of epoch, learning rate, batch size, step training, etc. However, to create a good system it took a long process such as taking dataset, labelling process, then training and testing models to gain the best performance. In this experiment, we analyze SSD method in AR technology using inception architecture as pre-trained Convolutional neural network (CNN), and then do transfer learning to minimize amount training time. The configuration that used is the number of step training. The result of this experiment gets the best accuracy in 70.17%. Then, the best performance is used as an object detection model for marker’s AR technology.Abstrak Saat ini, Artificial intelligence merupakan teknologi yang sedang berkembang pesat. Salah satunya adalah teknologi Augmented Reality (AR). AR adalah teknologi yang menggabungkan dunia nyata dengan virtual secara real-time dengan interaksi pengguna secara langsung dan menampilkannya dalam bentuk 3D. Teknologi AR ini memiliki dua metode yaitu dengan marker dan markerless. Dalam perkembangannya, AR berbasis marker membutuhkan sistem deteksi objek yang memiliki performa tinggi sebagai alat interaksi antara pengguna dengan perangkatnya. Single shot multibox detector (SSD) merupakan algoritma deteksi objek yang memiliki komputasi pembelajaran dan kinerja yang baik. Metode ini dipengaruhi oleh beberapa parameter seperti jumlah lapisan konvolusi, epoch, learning rate, jumlah batch, step training, dll. Namun, dalam mengimplementasikannya diperlukan proses yang cukup panjang seperti, pengambilan dataset, proses pelabelan, proses pelatihan menggunakan metode SSD, dan melakukan pengujian terhadap beberapa model untuk mencari perfomansi paling baik. Dalam percobaan ini, kami melakukan analisis terhadap metode SSD pada teknologi AR menggunakan arsitektur Inception sebagai pre-trained Convolutional neural network (CNN), kemudian dilakukan transfer learning untuk memperkecil jumlah kelas data pelatihan dan waktu pelatihan data. Konfigurasi yang digunakan berupa jumlah step pada pelatihan. Hasil dari penilitian ini menunjukan akurasi terbaik sebesar 70,17%. Kemudian, perfomansi terbaik digunakan sebagai model deteksi objek untuk marker pada teknologi AR.

Representing a Digital Twin City Model Using Open Source Tools and Integrating It With Dynamic Sensor Data

2021 ◽  
pp. 295-319
Author(s):  
Fabrizio Massara ◽  
Tatsiana Hubina ◽  
Sara Mannoni ◽  
Adelaide Ramassotto ◽  
Fabrizio Barbero
Keyword(s):  
Real Time ◽  
3D Models ◽  
Sensor Data ◽  
Time Data ◽  
Building Information Modelling ◽  
Twin City ◽  
Building Information ◽  
City Model ◽  
High Level ◽  
The City

This work presents the developments of representing a part of the city districts of Manchester, UK and Turin, IT initiated within the FP7 DIMMER project completed in 2016 and continued in the last years by the Center of Excellence GIS of CSI Piemonte. The DIMMER system integrates BIM (building information modelling) and district level 3D models with real-time data from sensors and user feedback to analyze and correlate buildings utilization and provide real-time feedback about energy-related behaviors. The emerging standard 3D Tiles endorsed by the OGC was applied to represent CityGML data of two districts of Turin, Italy and Manchester, UK. 3D Tiles allows for a high level of detail (LOD) visualization that displays increasing detail of geometric features and their alphanumeric properties. Currently, the OGC 3D Tiles technology is mature, and the OGC CityGML specification will be soon updated to version three, with the adoption of exciting innovations like the support of time-dependent properties defined Dynamizers.

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