Increasing the Tracking Efficiency of Mobile Augmented Reality Using a Hybrid Tracking Technique

Obeidy, Waqas Khalid; Arshad, Haslina; Chowdhury, Shahan Ahmad; Parhizkar, Behrang; Huang, Jiung-Yao

doi:10.1007/978-3-319-02958-0_41

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Registration strategies determine these geometric relations. Different registration techniques have been produced in the field of AR, and it is imperative to choose the proper tracking strategy for the application necessities [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Enhancement the Augmented Reality Framework Using Proposed Registration Method

Ali¹,

Abdulla²,

Ghindawi³

2017

JNUS

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The augmented reality field is a visualization technique using the computers, it provides an additional information to support the work of multiple applications. This research presents a proposed method to solve the registration problem in the augmented reality field. This method dealing with a special case of the registration process, when the virtual object registered with the real object, which sometime has an addition above it. It ensures the continuance of the registration process when there is an object exist on or pass through a real object. This method converting the image colors combining with using the interest features principles in order to implement an accurate registration process. This method work accurately to detect the right position and monitor the motion in the used video, depending on finding the important common features and geometric transformation principles.

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Section: Introductionmentioning

confidence: 99%

Enhancement the Augmented Reality Framework Using Proposed Registration Method

Ali¹,

Abdulla²,

Ghindawi³

2017

JNUS

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“…Tracking strategies decide these geometric relations. Different tracking techniques have been produced in the field of AR , and it is imperative to choose the proper tracking strategy for the application necessities [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Proposed Registration Method Using Tracking Interest Features for Augmented Reality

Abdulla¹,

Ali²,

Ghindawi³

2016

ETJ

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An Augmented Reality (AR) incorporates a mix of genuine and PC created scene segments. AR frameworks enhance a client's impression of this present reality with information that is not entirely of the scene. A key test for making an expanded the truth is to keep up precise arrangement amongst genuine and virtual thing.This exploration delineates a method to build up the enlistment extent of a dream based enlarged reality (AR) framework, also explores a simple method for detecting and tracking natural features in video stream. In this method, a reference image has been used as a tool to find the a proper position of an object. This method first uses Harris Corner Detector to detect the interest features and find the correspondences using crosscorrelation method then it used the Random Sample Consensus (RANSAC) algorithm to find the Homography matrix .After acquiring keypoints in the video frame, a Kanade-Lucas-Tomasi (KLT) feature tracker optical flow tracking algorithm has been used to track the motion of these keypoints frame-by-frame. By maintaining the correspondence between the tracked keypoints and those on the clean marker image, a new homography for every frame has been computed. This allows tracking the orientation of the marker as it moves in the video frames. Experiments for assessing the possibility of the technique are implemented in order to illustrate the potential benefits of the method, in which result's that to the target registration error ( TRE) reach 0.0020 , root mean square error (RMSE ) is 0.003 and average time for whole dataset is 2.5 s

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“…However, the tracking result solely relies on inertial sensing usually lacks of sufficient accuracy, especially as time increases the tracking error accumulates due to noises and bias in sensors. To address this problem, authors in [3,4] utilized inertial sensing only as an assistance of a visual tracker to guide fast feature extraction and matching, and to take over the tracking task when the visual tracker fails due to severe occlusions and motion blurs. However, large inertial tracking errors, which do occur from time to time for today's smartphones, result in instability of their system due to the sole reliance on the inertial tracker to guide the subsequent vision-based tracking.…”

Section: Introductionmentioning

confidence: 99%

Vision-Inertial Hybrid Tracking for Robust and Efficient Augmented Reality on Smartphones

Yang

Xue

et al. 2015

Proceedings of the 23rd ACM International Conference on Multimedia

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This paper aims at robust and efficient pose tracking for augmented reality on modern smartphones. Existing methods, relying on either vision analysis or motion sensing, are either too computationally expensive to achieve real-time performance on a smartphone, or too noisy to achieve sufficient robustness. This paper presents a hybrid tracking system which can achieve realtime performance with high robustness. Our system utilizes an efficient featureless method based on pixel-based registration to track the object pose on every frame. The featureless tracking result is revised from time to time by a feature-based method to reduce tracking errors. Both featureless and feature-based tracking results are sensitive to large motion blurs. To improve the robustness, an adaptive Kamlan filter is proposed to fuse the visual tracking results with the inertial tracking results computed form phone's built-in sensors. Our hybrid method is evaluated on a dataset consisting of 16 video clips with synchronized inertial sensing data. Experimental results demonstrated the superior performance of our method to state-of-the-art visual tracking methods [5, 12] on smartphones. The dataset will be made publicly available with the publication of this paper.

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Increasing the Tracking Efficiency of Mobile Augmented Reality Using a Hybrid Tracking Technique

Cited by 5 publications

References 19 publications

Enhancement the Augmented Reality Framework Using Proposed Registration Method

Enhancement the Augmented Reality Framework Using Proposed Registration Method

A Proposed Registration Method Using Tracking Interest Features for Augmented Reality

Vision-Inertial Hybrid Tracking for Robust and Efficient Augmented Reality on Smartphones

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