Real-Time Tracking Error Estimation for Augmented Reality for Registration with Linecode Markers

Bian, Zhiqiang; Ishii, Hirotake; Shimoda, Hiroshi; Izumi, Masanori

doi:10.1093/ietisy/e91-d.7.2041

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…During the detection, errors often arise because of camera vibration and constraints such as the limitations of image resolution, quantization errors, and lens distortions [5], [6]. Therefore, accurate error estimation is important in vehicle detection issues, and image processing techniques for position estimation or motion detection are necessary in many situations [7]- [10]. However, most of the previous studies have not involved methods of reducing errors caused by changes of camera parameters, while some important issues like error estimation and the way to set appropriate camera parameters were seldom considered.…”

Section: Introductionmentioning

confidence: 99%

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Lin

Chen

2009

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents new approaches for the estimation of range between the preceding vehicle and the experimental vehicle, estimation of vehicle size and its projective size, and dynamic camera calibration. First, our proposed approaches adopt a camera model to transform coordinates from the ground plane onto the image plane to estimate the relative position between the detected vehicle and the camera. Then, to estimate the actual and projective size of the preceding vehicle, we propose a new estimation method. This method can estimate the range from a preceding vehicle to the camera based on contact points between its tires and the ground and then estimate the actual size of the vehicle according to the positions of its vertexes in the image. Because the projective size of a vehicle varies with respect to its distance to the camera, we also present a simple and rapid method of estimating a vehicle's projective height, which allows a reduction in computational time for size estimation in real-time systems. Errors caused by the application of different camera parameters are also estimated and analyzed in this study. The estimation results are used to determine suitable parameters during camera installation to suppress estimation errors. Finally, to guarantee robustness of the detection system, a new efficient approach to dynamic calibration is presented to obtain accurate camera parameters, even when they are changed by camera vibration owing to on-road driving. Experimental results demonstrate that our approaches can provide accurate and robust estimation results of range and size of target vehicles.

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Section: Introductionmentioning

confidence: 99%

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Lin

Chen

2009

IEICE Trans. Inf. & Syst.

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A Useful Visualization Technique: A Literature Review for Augmented Reality and its Application, limitation & future direction

Dai

Jin

Luo

et al. 2009

Visual Information Communication

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Quantitative evaluation of overlaying discrepancies in mobile augmented reality applications for AEC/FM

Jáuregui

Manchado

Del-Castillo-Igareda³

et al. 2019

Advances in Engineering Software

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14Augmented Reality (AR) is a trending technology that provides a live view of the real and 15 physical environment augmented by virtual elements, enhancing the information of the scene 16 with digital information (sound, video, graphics, text or geo-location). Its application to 17 architecture, engineering and construction, and facility management (AEC/FM) is 18 straightforward and can be very useful to improve the on-site work at different stages of the 19 projects. However, one of the most important limitations of Mobile Augmented Reality (MAR) 20 is the lack of accuracy when the screen overlays the virtual models on the real images captured 21 by the camera. The main reasons are errors related to tracking (positioning and orientation of 22 the mobile device) and image capture and processing (projection and distortion issues). 23 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:// creativecommons.org/licenses/by-nc-nd/4.0/ This paper shows a new methodology to mathematically perform a quantitative evaluation, in 24 world coordinates, of those overlaying discrepancies on the screen, obtaining the real-scale 25 distances from any real point to the sightlines of its virtual projections for any AR application. 26Additionally, a new utility for filtering built-in sensor signals in mobile devices is presented: 27 the Drift-Vibration-Threshold function (DVT), a straightforward tool to filter the drift suffered 28 by most sensor-based tracking systems. 29 30 Engineering; AEC/FM; Geo-Location; Sensors. 33 34 However, the most sophisticated and up-to-date 3D techniques for designing, modelling and 46 representing construction projects have not been able to substitute definitely paper layouts 47on site yet. Digital devices, such as tablets, smartphones or laptops, are often used on site for 48 illustrating the traditional 2D blueprints that have traditionally been used in projects, usually 49 by means of 2D on-screen pdf or CAD files. Even though mobile computing is a field in 50 evolution, its possibilities are not widely spread in current practices. Human skills (e.g. spatial 51relations, spatial orientation, spatial visualization, etc.) are still required for processing the 2D 52 documents and understanding their meaning in real world, i.e. interpreting classical 2D 53 representations to recognize their 3D implications. These limitations can be overcome by 54 means of adequate technologies, for example the Augmented Reality. 55This paper presents an efficient solution for developing an outdoor application for portable 56 devices, based on Mobile Augmented Reality (MAR), to represent the virtual model of a 57 project in its construction site. As will be explained throughout this work, there are different 58 factors that affect the accuracy and efficiency of this technology, i.e. the geo-location of the 59 mobile device, its orientation, and the techniques for accurately overlaying virtual models 60 (dealing with projection and distortion issues), which motivated the authors to o...

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Real-Time Tracking Error Estimation for Augmented Reality for Registration with Linecode Markers

Cited by 3 publications

References 13 publications

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

A Useful Visualization Technique: A Literature Review for Augmented Reality and its Application, limitation & future direction

Quantitative evaluation of overlaying discrepancies in mobile augmented reality applications for AEC/FM

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