Pose tracking from natural features on mobile phones

Wagner, Daniel; Reitmayr, Gerhard; Mulloni, Alessandro; Drummond, Tom; Schmalstieg, Dieter

doi:10.1109/ismar.2008.4637338

Cited by 351 publications

(215 citation statements)

References 21 publications

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“…By presenting the user is difficult to observe directly, or through a 3-D virtual objects and difficult part represented by book learning can be enhanced by causing the user's attention and interest. To find the characteristics of the object by using a camera mounted on a mobile device to implement the augmented reality on the mobile device to obtain the desired coordinate position through this synthesis is that the object [7].…”

Section: Augmented Realitymentioning

confidence: 99%

A study on the Application of Augmented Reality Technology Exhibition Environment

Lee¹,

Kwon

2015

Journal of Digital Contents Society

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Section: Augmented Realitymentioning

confidence: 99%

A study on the Application of Augmented Reality Technology Exhibition Environment

Lee¹,

Kwon

2015

Journal of Digital Contents Society

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show abstract

“…With the introduction of highly capable smartphones, there has been much recent interest in combining tracking and content recognition for MAR [2,3,4,5,6,1]. Systems have been proposed to track video content using feature descriptors, including motion vector tracking [7], SURFTrac [2], and patch tracking [5].…”

Section: Prior Workmentioning

confidence: 99%

“…Systems have been proposed to track video content using feature descriptors, including motion vector tracking [7], SURFTrac [2], and patch tracking [5]. There have been numerous systems for image and video content recognition using natural feature descriptors.…”

Section: Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features

Takacs

Chandrasekhar

Tsai

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

111

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We present a method that unifies tracking and video content recognition with applications to Mobile Augmented Reality (MAR). We introduce the Radial Gradient Transform (RGT) and an approximate RGT, yielding the Rotation-Invariant, Fast Feature (RIFF) descriptor. We demonstrate that RIFF is fast enough for real-time tracking, while robust enough for large scale retrieval tasks. At 26× the speed, our trackingscheme obtains a more accurate global affine motionmodel than the Kanade Lucas Tomasi (KLT) tracker. The same descriptors can achieve 94% retrieval accuracy from a database of 10 4 images.

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“…The RTs technique implements corresponding point search as decision tree traversal and is capable of high-speed classification of keypoints. In recent years, this method has been developed further, and there are reports that it can even run on low-memory mobile devices [12], [13]. However, methods based on Ref.…”

Section: Introductionmentioning

confidence: 99%

Keypoint Recognition with Two-Stage Randomized Trees

Shimizu

Fujiyoshi

2012

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a high-precision, high-speed keypoint matching method using two-stage randomized trees (RTs). The keypoint classification uses conventional RTs for high-precision, real-time keypoint matching. However, the wide variety of view transformations for templates expressed by RTs make it diffidult to achieve high-precision classification for all transformations with a single RTs. To solve this problem, the proposed method classifies the template view transformations in the first stage and then, in the second stage, classifies the keypoints using the RTs that corresponds to each of the view transformations classified in the first stage. Testing demonstrated that the proposed method is 88.5% more precise than SIFT, and 63.5% more precise than using conventional RTs for images in which the viewpoint of the object is rotated by 70 degrees. We have also shown that the proposed method supports real-time keypoint matching at 12 fps.

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Pose tracking from natural features on mobile phones

Cited by 351 publications

References 21 publications

A study on the Application of Augmented Reality Technology Exhibition Environment

A study on the Application of Augmented Reality Technology Exhibition Environment

Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features

Keypoint Recognition with Two-Stage Randomized Trees

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