Marker-less vision based tracking for mobile augmented reality

Beier, David; Billert, R.; Brüderlin, Beat; Stichling, Dirk; Kleinjohann, Bernd

doi:10.1109/ismar.2003.1240709

Cited by 22 publications

(22 citation statements)

References 2 publications

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“…The approaches described in Genc et al (2002), Chia et al, (2002) and Beier et al (2003) are model-based marker-less AR tracking systems using a priori known 3D models. In Genc et al (2002) a system for feature-based pose estimation in video streams without any visual markers is described.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Camera Tracking for Mobile Devices: The VisiTrack System

et al. 2006

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This paper presents VisiTrack, a novel approach for video based incremental tracking in real-time. The major objectives in the development of VisiTrack was to design or select algorithms that are well suited for embedded real-time computation. We had a special focus on latency reduction and storage minimization since the algorithms should run on mobile devices like PDAs with the appropriate extension, i.e. mainly a camera, in real-time. The image analysis, camera localization and feature position approximation of VisiTrack are explained in detail. The CV-SDF model, an extension of Synchronous Dataflow graphs (SDF), supporting the principles of linear processing and fine-grained pipelining was defined and applied for the design of all VisiTrack modules in order to fulfill real-time constraints and reduce system latency. Furthermore the camera localization and position approximation include mechanisms for minimization of errors that may arise for instance due to measurement inaccuracies. Current applications of VisiTrack in the augmented reality domain and robotic self localization show its good performance. However VisiTrack is not limited to these application domains.

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Section: Related Workmentioning

confidence: 99%

Real-Time Camera Tracking for Mobile Devices: The VisiTrack System

et al. 2006

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“…Object tracking techniques can be divided into two main streams: marker-based object tracking [12], [13], [14] and marker-less-based object tracking [15]. General tracking technologies include mechanical arms and linkages: accelerometers and gyroscopes, magnetic £elds, radio frequency signals, and acoustics.…”

Section: Object Trackingmentioning

confidence: 99%

ARCADE: Augmented Reality Computing Arena for Digital Entertainment

Lyu

King

Wong

et al. 2005

2005 IEEE Aerospace Conference

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Abstract-Augmented Reality (AR) technology for digital composition of animation with real scenes is to bring new digital entertainment experience to the viewers. Augmented Reality is a form of human-machine interaction. The key feature of the Augmented Reality technology is to present auxiliary information in the £eld of view for an individual automatically without human intervention. The effect is similar to composing computer-animated images with real scenes. To achieve the new Augmented Reality experience, two main problems are: How to keep track of the viewing parameters of the individual viewer? How to render a virtual image in the £eld of view correctly and seamlessly? To tackle the above, we are designing and implementing an enabling framework, called Augmented Reality Computing Arena for Digital Entertainment (ARCADE), to support the creation of Augmented Reality entertainment applications. Moreover, we are also developing two new video object tracking algorithms, 3D surface markers tracking and human head and face tracking. In this paper, ARCADE's system architecture and its components are described in detail and illustrated with an application example.

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“…However, most vision based augmented reality applications have to train physical objects patterns or paper markers before we use it [6]. Thus, if the physical objects for the AR application has destroyed or damaged, users have to buy new object or print it out again.…”

Section: Introductionmentioning

confidence: 99%