Extrinsic Camera Parameter Recovery from Multiple Image Sequences Captured by an Omni-Directional Multi-camera System

Sato, Tomokazu; Ikeda, Sei; Yokoya, Naokazu

doi:10.1007/978-3-540-24671-8_26

Cited by 39 publications

(43 citation statements)

References 22 publications

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“…The linear dynamic model for the new feature in the main filter is the same as model (10). What we must do is only to append model (10) to the linear dynamic model of the main filter and model (11) to model (6). Then, the additional filter for the jth feature is removed and the state vector and state covariance matrix estimated up to now by the additional filter corresponding to the jth feature are inserted in to the main filter.…”

Section: Initialization Of New Point Features Appearingmentioning

confidence: 99%

“…The shortcomings of this system are a cumbersome mechanism and a finite time required to capture an image with a wide angular range. Finally, we can adopt a multi-camera system [10], [2], [9]. The cameras constituting a multi-camera system are arranged so that there is no or small intersection of the fields of view.…”

Section: Introductionmentioning

confidence: 99%

“…Motion estimation algorithms for multi-camera system in [2], and [9] were designed to obtain differential motion estimates or for a twoview framework. Recently, an approach acquiring the camera motion parameter by using a multi-camera system for long term image sequences was suggested by Sato et al [10]. However, this method is a batch method and requires some features of known 3-D positions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sequential Motion and Scene Reconstruction from Image Sequences Captured by a Multi-camera System

Kim¹,

Chung²,

Park³

2006

Procedings of the British Machine Vision Conference 2006

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This paper deals with structure and motion estimation by using image sequences captured by a multi-camera system moving in a scene. Single camera systems with a relatively small field of view have limited accuracy because of the inherent confusion between translation and rotation. This is also the case for the stereo camera systems if the systems need the intersection of the fields of view for stereo correspondence. The cameras constituting the multi-camera system considered ultimately in this paper are arranged so that there is no, or a small, intersection of the fields of view. This configuration improves the accuracy of the structure and motion estimation because the ambiguity mentioned above decreases due to a large field of view. We propose a recursive algorithm for real time applications of the multi-camera system using long term image sequences, which have appearing and disappearing point features. The results of the experiments with long term real image sequences are presented to demonstrate the feasibility of the algorithm.

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Section: Initialization Of New Point Features Appearingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sequential Motion and Scene Reconstruction from Image Sequences Captured by a Multi-camera System

Kim¹,

Chung²,

Park³

2006

Procedings of the British Machine Vision Conference 2006

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show abstract

“…Sequence Our extrinsic camera parameter estimation is based on structurefrom-motion [14]. In this method, first, markers and natural features in the image sequences captured by an omni-directional multi-camera system are automatically tracked and then the reprojection errors are minimized throughout the sequences.…”

Section: -D Reconstruction Of the Environment From Omni-directional mentioning

confidence: 99%

“…Captured image sequence consists of 1,250 frames long with 6 images per each frame(totally 7,500 images). Then, landmark database is created by estimating camera path and 3-D coordinates of natural features [14]. For every landmark, multi-scale image template with three different scales of 15 × 15 pixels each, is created per each camera position.…”

Section: Experiments In An Outdoor Environmentmentioning

confidence: 99%

Estimating Camera Position and Posture by Using Feature Landmark Database

Sato

Yokoya

2005

Image Analysis

Self Cite

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Abstract. Estimating camera position and posture can be applied to the fields of augmented reality and robot navigation. In these fields, to obtain absolute position and posture of the camera, sensor-based methods using GPS and magnetic sensors and vision-based methods using input images from the camera have been investigated. However, sensor-based methods are difficult to synchronize the camera and sensors accurately, and usable environments are limited according to selection of sensors. On the other hand, vision-based methods need to allocate many artificial markers otherwise an estimation error will accumulate. Thus, it is difficult to use such methods in large and natural environments. This paper proposes a vision-based camera position and posture estimation method for large environments, which does not require sensors and artificial markers by detecting natural feature points from image sequences taken beforehand and using them as landmarks.

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Initial Closed-Form Solution to Mapping from Unknown Planar Motion of an Omni-directional Vision Sensor

Kim

Choi

2014

Advances in Visual Computing

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Extrinsic Camera Parameter Recovery from Multiple Image Sequences Captured by an Omni-Directional Multi-camera System

Cited by 39 publications

References 22 publications

Sequential Motion and Scene Reconstruction from Image Sequences Captured by a Multi-camera System

Sequential Motion and Scene Reconstruction from Image Sequences Captured by a Multi-camera System

Estimating Camera Position and Posture by Using Feature Landmark Database

Initial Closed-Form Solution to Mapping from Unknown Planar Motion of an Omni-directional Vision Sensor

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