scholarly journals Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering

2021 ◽  
Vol 11 (18) ◽  
pp. 8750
Author(s):  
Styliani Verykokou ◽  
Argyro-Maria Boutsi ◽  
Charalabos Ioannidis
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Image Matching ◽  
Mobile Augmented Reality ◽  
Surface Recognition ◽  
Wide Range ◽  
Speed Up ◽  
Time Required ◽  
Fast Rendering ◽  
Textured Models

Mobile Augmented Reality (MAR) is designed to keep pace with high-end mobile computing and their powerful sensors. This evolution excludes users with low-end devices and network constraints. This article presents ModAR, a hybrid Android prototype that expands the MAR experience to the aforementioned target group. It combines feature-based image matching and pose estimation with fast rendering of 3D textured models. Planar objects of the real environment are used as pattern images for overlaying users’ meshes or the app’s default ones. Since ModAR is based on the OpenCV C++ library at Android NDK and OpenGL ES 2.0 graphics API, there are no dependencies on additional software, operating system version or model-specific hardware. The developed 3D graphics engine implements optimized vertex-data rendering with a combination of data grouping, synchronization, sub-texture compression and instancing for limited CPU/GPU resources and a single-threaded approach. It achieves up to 3 × speed-up compared to standard index rendering, and AR overlay of a 50 K vertices 3D model in less than 30 s. Several deployment scenarios on pose estimation demonstrate that the oriented FAST detector with an upper threshold of features per frame combined with the ORB descriptor yield best results in terms of robustness and efficiency, achieving a 90% reduction of image matching time compared to the time required by the AGAST detector and the BRISK descriptor, corresponding to pattern recognition accuracy of above 90% for a wide range of scale changes, regardless of any in-plane rotations and partial occlusions of the pattern.

INTERFRAME CODING OF CANONICAL PATCHES FOR LOW BIT-RATE MOBILE AUGMENTED REALITY

2013 ◽  
Vol 07 (01) ◽  
pp. 5-24 ◽  
Author(s):  
MINA MAKAR ◽  
SAM S. TSAI ◽  
VIJAY CHANDRASEKHAR ◽  
DAVID CHEN ◽  
BERND GIROD
Keyword(s):  
Augmented Reality ◽  
Image Matching ◽  
Predictive Coding ◽  
Mobile Augmented Reality ◽  
Bit Rate ◽  
Low Bit Rate ◽  
Feature Descriptors ◽  
Continuous Stream ◽  
Interframe Coding ◽  
Matching Performance

Local features are widely used for content-based image retrieval and augmented reality applications. Typically, feature descriptors are calculated from the gradients of a canonical patch around a repeatable keypoint in the image. In this paper, we propose a temporally coherent keypoint detector and design efficient interframe predictive coding techniques for canonical patches and keypoint locations. In the proposed system, we strive to transmit each patch with as few bits as possible by simply modifying a previously transmitted patch. This enables server-based mobile augmented reality where a continuous stream of salient information, sufficient for image-based retrieval and localization, can be sent over a wireless link at a low bit-rate. Experimental results show that our technique achieves a similar image matching performance at 1/15 of the bit-rate when compared to detecting keypoints independently frame-by-frame and allows performing streaming mobile augmented reality at low bit-rates of about 20–50 kbps, practical for today's wireless links.

scholarly journals Iterative Pose Refinement for Object Pose Estimation Based on RGBD Data

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4114
Author(s):  
Shao-Kang Huang ◽  
Chen-Chien Hsu ◽  
Wei-Yen Wang ◽  
Cheng-Hung Lin
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
State Of The Art ◽  
Iterative Refinement ◽  
Accurate Estimation ◽  
Geometric Features ◽  
Wide Range ◽  
Final Output ◽  
Refinement Process ◽  
Object Pose Estimation

Accurate estimation of 3D object pose is highly desirable in a wide range of applications, such as robotics and augmented reality. Although significant advancement has been made for pose estimation, there is room for further improvement. Recent pose estimation systems utilize an iterative refinement process to revise the predicted pose to obtain a better final output. However, such refinement process only takes account of geometric features for pose revision during the iteration. Motivated by this approach, this paper designs a novel iterative refinement process that deals with both color and geometric features for object pose refinement. Experiments show that the proposed method is able to reach 94.74% and 93.2% in ADD(-S) metric with only 2 iterations, outperforming the state-of-the-art methods on the LINEMOD and YCB-Video datasets, respectively.

scholarly journals DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

2021 ◽  
Vol 1 ◽  
pp. 87
Author(s):  
Konstantinos C. Apostolakis ◽  
Nikolaos Dimitriou ◽  
George Margetis ◽  
Stavroula Ntoa ◽  
Dimitrios Tzovaras ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Computer Vision ◽  
Law Enforcement ◽  
Augmented Reality ◽  
Pose Estimation ◽  
Situational Awareness ◽  
Estimation Method ◽  
Violence Detection ◽  
Wide Range

Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains. Their potential has not gone unnoticed in the security sector with several law enforcement agencies (LEAs) employing AI applications in their daily operations for forensics and surveillance. In this paper, we present the DARLENE ecosystem, which aims to bridge existing gaps in applying AR and AI technologies for rapid tactical decision-making in situ with minimal error margin, thus enhancing LEAs’ efficiency and Situational Awareness (SA). Methods: DARLENE incorporates novel AI techniques for computer vision tasks such as activity recognition and pose estimation, while also building an AR framework for visualization of the inferenced results via dynamic content adaptation according to each individual officer’s stress level and current context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results and will constitute the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.

A 27 mW Reconfigurable Marker-Less Logarithmic Camera Pose Estimation Engine for Mobile Augmented Reality Processor

2015 ◽  
Vol 50 (11) ◽  
pp. 2513-2523 ◽  
Author(s):  
Injoon Hong ◽  
Gyeonghoon Kim ◽  
Youchang Kim ◽  
Donghyun Kim ◽  
Byeong-Gyu Nam ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Mobile Augmented Reality ◽  
Camera Pose Estimation ◽  
Camera Pose

scholarly journals Modelling and optimization of computer network traffic controllers

2005 ◽  
Vol 2005 (6) ◽  
pp. 617-640 ◽  
Author(s):  
N. U. Ahmed ◽  
Bo Li ◽  
Luis Orozco-Barbosa
Keyword(s):  
Genetic Algorithm ◽  
Network Traffic ◽  
Computer Network ◽  
Formal Model ◽  
Computation Time ◽  
Wide Range ◽  
Token Bucket ◽  
Speed Up ◽  
Genetic Algorithm Approach ◽  
Time Required

During the past years, there has been increasing interest in the design and development of network traffic controllers capable of ensuring the QoS requirements of a wide range of applications. In this paper, we construct a dynamic model for the token-bucket algorithm: a traffic controller widely used in various QoS-aware protocol architectures. Based on our previous work, we use a system approach to develop a formal model of the traffic controller. This model serves as a basis to formally specify and evaluate the operation of the token-bucket algorithm. Then we develop an optimization algorithm based on a dynamic programming and genetic algorithm approach. We conduct an extensive campaign of numerical experiments allowing us to gain insight on the operation of the controller and evaluate the benefits of using a genetic algorithm approach to speed up the optimization process. Our results show that the use of the genetic algorithm proves particularly useful in reducing the computation time required to optimize the operation of a system consisting of multiple token-bucket-regulated sources.

scholarly journals Investigating the Learning Process of Folk Dances Using Mobile Augmented Reality

2020 ◽  
Vol 10 (2) ◽  
pp. 599
Author(s):  
Iris Kico ◽  
Fotis Liarokapis
Keyword(s):  
Augmented Reality ◽  
Learning Process ◽  
Traditional Teaching ◽  
Folk Dance ◽  
Mobile Augmented Reality ◽  
Projection System ◽  
Video Recordings ◽  
Speed Up ◽  
Virtual And Augmented Reality ◽  
Professional Dancers

Learning how to dance is not an easy task and traditional teaching methods are the main approach. Digital technologies (such as video recordings of dances) have already been successfully used in combination with the traditional methods. However, there are other emerging technologies such as virtual and augmented reality that have the potential of providing greater assistance, in order to speed up the process as well as assisting the learners. This paper presents a prototype mobile augmented reality application for assisting the process of learning folk dances. Initially, a folk dance was digitized based on recordings from professional dancers. Avatar representations (of either male or female) are synchronized with the digital representation of the dance. To assess the effectiveness of mobile augmented reality, it was comparatively evaluated with a large back-projection system in laboratory conditions. Twenty healthy participants took part in the study, and their movements were captured using motion capture system and then compared with the recordings from the professional dancers. Experimental results indicate that augmented reality (AR) application has the potential to be used for learning process.

scholarly journals Adding Active Slot Joint Larger Broadcast Radius for Fast Code Dissemination in WSNs

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4055 ◽  
Author(s):  
Wei Yang ◽  
Wei Liu ◽  
Zhiwen Zeng ◽  
Anfeng Liu ◽  
Guosheng Huang ◽  
...  
Keyword(s):  
Diffusion Path ◽  
Program Code ◽  
Wide Range ◽  
Code Dissemination ◽  
Speed Up ◽  
Dissemination Process ◽  
Balance Energy ◽  
Time Required ◽  
Performance Analysis And Simulation ◽  
Improved Methods

By using Software Defined Network (SDN) technology, senor nodes can get updated program code which can provide new features, so it has received extensive attention. How to effectively spread code to each node fast is a challenge issue in wireless sensor networks (WSNs). In this paper, an Adding Active Slot joint Larger Broadcast Radius (AAS-LBR) scheme is proposed for fast code dissemination. The AAS-LBR scheme combines the energy of data collection and code dissemination, making full use of the remaining energy in the far-sink area to increase the active slot and the broadcast radius to speed up the code dissemination. The main contributions of the proposed AAS-LBR scheme are the following: (1) Make full use of the remaining energy of the far sink area to expand the broadcast radius, so that the node broadcasts a longer distance. The wide range of broadcasts makes the number of nodes receiving code more, which speeds up the spread of code dissemination. (2) AAS-LBR uses two improved methods to further reduce the number of broadcasts and speed up the code dissemination: (a) When constructing the broadcast backbone whose nodes dominate all nodes in network and are responsible for broadcasting code, the active slot is added to the next hop node in a pipeline style on the diffusion path, which enables the code dissemination process to continue without pause. Thus, the code can quickly spread to the entire broadcast backbone. (b) For the nodes in the non-broadcast backbone whose nodes are dominated by the broadcast backbone and only for receiving code, an active slot is added coincident with its broadcast backbone’ active slot, which can reduce the time required for code dissemination and reduce the number of broadcasts. A lot of performance analysis and simulation results show that compared to previous schemed, the AAS-LBR scheme can balance energy consumption, the transmission delay can be reduced 43.09–78.69%, the number of broadcasts can be reduced 44.51–86.18% and the energy efficiency is improved by about 24.5%.

Shape Recognition and Pose Estimation for Mobile Augmented Reality

2011 ◽  
Vol 17 (10) ◽  
pp. 1369-1379 ◽  
Author(s):  
Nate Hagbi ◽  
Oriel Bergig ◽  
Jihad El-Sana ◽  
Mark Billinghurst
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Shape Recognition ◽  
Mobile Augmented Reality
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