Multiple 3D Object tracking for augmented reality

We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well with the number of objects, and a proper wa...

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Veröffentlicht in:2008 7th IEEE International Symposium on Mixed and Augmented Reality S. 117 - 120
Hauptverfasser: Park, Youngmin, Lepetit, Vincent, Woontack Woo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 15.09.2008
IEEE
Schriftenreihe:ACM Other Conferences
Schlagworte:
Augmented Reality
Computer Vision
Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems
Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems > Object recognition
Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems > Tracking
Computing methodologies > Artificial intelligence > Computer vision > Computer vision tasks > Scene understanding
Computing methodologies > Artificial intelligence > Computer vision > Image and video acquisition > 3D imaging
Computing methodologies > Computer graphics > Animation
Computing methodologies > Machine learning
Feature extraction
Object detection
Robustness
Solid modeling
Target tracking
Three dimensional displays
Tracking
ISBN:9781424428403, 1424428408
Online-Zugang:Volltext
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Abstract We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well with the number of objects, and a proper way to deal with several objects is required. Our method combines object detection and tracking: Frame-to-frame tracking is less computationally demanding but is prone to fail, while detection is more robust but slower. We show how to combine them to take the advantages of the two approaches, and demonstrate our method on several real sequences.
AbstractList We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well with the number of objects, and a proper way to deal with several objects is required. Our method combines object detection and tracking: Frame-to-frame tracking is less computationally demanding but is prone to fail, while detection is more robust but slower. We show how to combine them to take the advantages of the two approaches, and demonstrate our method on several real sequences.
Author Lepetit, Vincent
Park, Youngmin
Woontack Woo
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  surname: Woontack Woo
  fullname: Woontack Woo
  organization: GIST, U-VR Lab, Korea
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PublicationTitle 2008 7th IEEE International Symposium on Mixed and Augmented Reality
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Snippet We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider...
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StartPage 117
SubjectTerms Augmented Reality
Computer Vision
Computing methodologies -- Artificial intelligence -- Computer vision -- Computer vision problems
Computing methodologies -- Artificial intelligence -- Computer vision -- Computer vision problems -- Object recognition
Computing methodologies -- Artificial intelligence -- Computer vision -- Computer vision problems -- Tracking
Computing methodologies -- Artificial intelligence -- Computer vision -- Computer vision tasks -- Scene understanding
Computing methodologies -- Artificial intelligence -- Computer vision -- Image and video acquisition -- 3D imaging
Computing methodologies -- Computer graphics -- Animation
Computing methodologies -- Machine learning
Feature extraction
Object detection
Robustness
Solid modeling
Target tracking
Three dimensional displays
Tracking
Title Multiple 3D Object tracking for augmented reality
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