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Composing the Feature Map Retrieval Process for Robust and Ready-to-Use Monocular Tracking

Wientapper, Folker ; Wuest, Harald ; Kuijper, Arjan (2011):
Composing the Feature Map Retrieval Process for Robust and Ready-to-Use Monocular Tracking.
In: Computers & Graphics, 35 (4), pp. 778-788. DOI: 10.1016/j.cag.2011.04.008,
[Article]

Abstract

This paper focuses on the preparative process of natural feature map retrieval for a mobile camera-based tracking system. We cover the most important aspects of a general purpose tracking system including the acquisition of the scene's geometry, tracking initialization and fast and accurate frame-by-frame tracking. To this end, several state-of-the-art techniques - each targeted at one particular subproblem - are fused together, whereby their interplay and complementary benefits form the core of the system and the thread of our discussion. The choice of the individual sub-algorithms in our system reflects the scarcity of computational resources on mobile devices. In order to allow a more accurate, more robust and faster tracking during run-time, we therefore transfer the computational load into the preparative customization step wherever possible. From the viewpoint of the user, the preparative stage is kept very simple. It only involves recording the scene from various viewpoints and defining a transformation into a target coordinate frame via manual definition of only a few 3D to 3D point correspondences. Technically, the image sequence is used to (1) capture the scene's geometry by a SLAM-Method and subsequent refinement via constrained Bundle Adjustment, (2) to train a Randomized-Trees classifier for wide-baseline tracking initialization, and (3) to analyze the view-point dependent visibility of each feature. During run-time, robustness and performance of the frame-to-frame tracking are further increased by fusing inertial measurements within a combined pose estimation.

Item Type: Article
Erschienen: 2011
Creators: Wientapper, Folker ; Wuest, Harald ; Kuijper, Arjan
Title: Composing the Feature Map Retrieval Process for Robust and Ready-to-Use Monocular Tracking
Language: English
Abstract:

This paper focuses on the preparative process of natural feature map retrieval for a mobile camera-based tracking system. We cover the most important aspects of a general purpose tracking system including the acquisition of the scene's geometry, tracking initialization and fast and accurate frame-by-frame tracking. To this end, several state-of-the-art techniques - each targeted at one particular subproblem - are fused together, whereby their interplay and complementary benefits form the core of the system and the thread of our discussion. The choice of the individual sub-algorithms in our system reflects the scarcity of computational resources on mobile devices. In order to allow a more accurate, more robust and faster tracking during run-time, we therefore transfer the computational load into the preparative customization step wherever possible. From the viewpoint of the user, the preparative stage is kept very simple. It only involves recording the scene from various viewpoints and defining a transformation into a target coordinate frame via manual definition of only a few 3D to 3D point correspondences. Technically, the image sequence is used to (1) capture the scene's geometry by a SLAM-Method and subsequent refinement via constrained Bundle Adjustment, (2) to train a Randomized-Trees classifier for wide-baseline tracking initialization, and (3) to analyze the view-point dependent visibility of each feature. During run-time, robustness and performance of the frame-to-frame tracking are further increased by fusing inertial measurements within a combined pose estimation.

Journal or Publication Title: Computers & Graphics
Journal Volume: 35
Issue Number: 4
Uncontrolled Keywords: Business Field: Digital society, Business Field: Virtual engineering, Research Area: Confluence of graphics and vision, Augmented reality (AR), Tracking, Sensor fusion, Feature map alignment, Feature management, Wide-baseline feature learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1016/j.cag.2011.04.008
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