Real-time vision-based camera tracking for augmented reality applications

Koller, Dieter; Klinker, Gudrun; Rose, E.; Breen, David E.; Whitaker, Ross T.; Tüceryan, Mihran

doi:10.1145/261135.261152

Cited by 107 publications

(58 citation statements)

References 14 publications

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“…The fiducials consisted of black disks on a white background, and sample images were collected varying their perspective, scale, and lighting conditions, as well as negative training images. Koller et al (1997) introduced square, black-on-white fiducials, which contained small red squares for their identification. Planar rectangular fiducials were also used in ARToolKit (Kato & Billinghurst, 1999).…”

Section: Tracking and Registration For Augmented Realitymentioning

confidence: 99%

A comparative study of the sense of presence and anxiety in an invisible marker versus a marker augmented reality system for the treatment of phobia towards small animals

Juan

Joele

2011

International Journal of Human-Computer Studies

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Section: Tracking and Registration For Augmented Realitymentioning

confidence: 99%

A comparative study of the sense of presence and anxiety in an invisible marker versus a marker augmented reality system for the treatment of phobia towards small animals

Juan

Joele

2011

International Journal of Human-Computer Studies

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“…By contrast, [71] introduces squared, black on white, fiducials, which contain small red squares for identification purposes. The corners are found by fitting straight line segments to the maximum gradient points on the border of the fiducial.…”

Section: Planar Fiducialsmentioning

confidence: 99%

Monocular Model-Based 3D Tracking of Rigid Objects: A Survey

Lepetit¹,

Fua²

2005

FNT in Computer Graphics and Vision

456

189

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Many applications require tracking of complex 3D objects. These include visual servoing of robotic arms on specific target objects, Augmented Reality systems that require real-time registration of the object to be augmented, and head tracking systems that sophisticated interfaces can use. Computer Vision offers solutions that are cheap, practical and non-invasive.This survey reviews the different techniques and approaches that have been developed by industry and research. First, important mathematical tools are introduced: Camera representation, robust estimation and uncertainty estimation. Then a comprehensive study is given of the numerous approaches developed by the Augmented Reality and Robotics communities, beginning with those that are based on point or planar fiducial marks and moving on to those that avoid the need to engineer the environment by relying on natural features such as edges, texture or interest. Recent advances that avoid manual initialization and failures due to fast motion are also presented. The survery concludes with the different possible choices that should be made when implementing a 3D tracking system and a discussion of the future of vision-based 3D tracking.Because it encompasses many computer vision techniques from lowlevel vision to 3D geometry and includes a comprehensive study of the massive literature on the subject, this survey should be the handbook of the student, the researcher, or the engineer who wants to implement a 3D tracking system.

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“…The head pose detection and recognition system has found wider application areas such as face recognition, action recognition, gait recognition, head recognition, and hand recognition systems [20][21][22][23]. In such systems, several sensors like binary, digital, and depth cameras are used to train and detect postures [24][25][26][27][28][29][30]. In such systems, several machine learning feature extraction algorithms and classification methods are implemented for detection and recognition of gestures.…”

Section: Introductionmentioning

confidence: 99%

Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning

et al. 2018

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Abstract:Extensive research has been conducted in human head pose detection systems and several applications have been identified to deploy such systems. Deep learning based head pose detection is one such method which has been studied for several decades and reports high success rates during implementation. Across several pet robots designed and developed for various needs, there is a complete absence of wearable pet robots and head pose detection models in wearable pet robots. Designing a wearable pet robot capable of head pose detection can provide more opportunities for research and development of such systems. In this paper, we present a novel head pose detection system for a wearable parrot-inspired pet robot using images taken from the wearer's shoulder. This is the first time head pose detection has been studied in wearable robots and using images from a side angle. In this study, we used AlexNet convolutional neural network architecture trained on the images from the database for the head pose detection system. The system was tested with 250 images and resulted in an accuracy of 94.4% across five head poses, namely left, left intermediate, straight, right, and right intermediate.

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Real-time vision-based camera tracking for augmented reality applications

Cited by 107 publications

References 14 publications

A comparative study of the sense of presence and anxiety in an invisible marker versus a marker augmented reality system for the treatment of phobia towards small animals

A comparative study of the sense of presence and anxiety in an invisible marker versus a marker augmented reality system for the treatment of phobia towards small animals

Monocular Model-Based 3D Tracking of Rigid Objects: A Survey

Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning

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