Robust Linear Auto-calibration of a Moving Camera from Image Sequences

Thormählen, Thorsten; Broszio, Hellward; Mikulastik, Patrick

doi:10.1007/11612704_8

Cited by 8 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The assumption that H is a similarity transformation is valid, if the camera matrices A k and 3D object points P j are reconstructed in the metric and not in the projective space by the structure-from-motion algorithm. In our experiments, we found that this is a valid approximation after auto-calibration [13], even if drift is present in the reconstruction.…”

Section: Finding Unconnected Feature Tracks Candidatesmentioning

confidence: 51%

Merging of feature tracks for camera motion estimation from video

Thormählen

Hasler

Wand

et al. 2008

IET 5th European Conference on Visual Media Production (CVMP 2008)

View full text Add to dashboard Cite

In this paper different application scenarios are presented for which the merging of unconnected feature point tracks is essential for successful camera motion estimation and 3D reconstruction from video. The first application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. Three experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

show abstract

Section: Finding Unconnected Feature Tracks Candidatesmentioning

confidence: 51%

Merging of feature tracks for camera motion estimation from video

Thormählen

Hasler

Wand

et al. 2008

IET 5th European Conference on Visual Media Production (CVMP 2008)

View full text Add to dashboard Cite

show abstract

“…The auto calibration in this article uses the standard projective parameters of the focal distance and the principal point with two coefficients of the radial distortion. [49] A solid linear method for the selfcalibration of a moving camera starting from a sequence of images is presented. The proposed approach uses known linear equations which are weighted by variable factors.…”

Section: Survey Of the Previous Workmentioning

confidence: 99%

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

et al. 2013

View full text Add to dashboard Cite

Abstract-In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

show abstract

“…There are well established procedures to determine the intrinsic and extrinsic camera parameters of the imaging system, see e.g. [14] and [38]. The true value of the extrinsic camera parameters, i.e.…”

Section: Validationmentioning

confidence: 99%

“…The size of the 3D models is inferred from vehicle velocity data obtained from on board wheel speed sensors. For more information regarding VCT, see [38] and [26].…”

Section: Camera Trackingmentioning

confidence: 99%

Using Augmentation Techniques for Performance Evaluation in Automotive Safety

Nilsson

Odblom

Fredriksson

et al. 2011

Handbook of Augmented Reality

View full text Add to dashboard Cite

This chapter describes a framework which uses augmentation techniques for performance evaluation of mobile computer vision systems. Computer vision systems use primarily image data to interpret the surrounding world, e.g. to detect, classify and track objects. The performance of mobile computer vision systems acting in unknown environments is inherently difficult to evaluate since, often, obtaining ground truth data is problematic. The proposed novel framework exploits the possibility to add new agents into a real data sequence collected in an unknown environment, thus making it possible to efficiently create augmented data sequences, including ground truth, to be used for performance evaluation. Varying the content in the data sequence by adding different agents or changing the behavior of an agent is straightforward, making the proposed framework very flexible. A key driver for using augmentation techniques to address computer vision performance is that the vision system output may be sensitive to the background data content. The method has been implemented and tested on a pedestrian detection system used for automotive collision avoidance. Results show that the method has potential to replace and complement physical testing, for instance by creating collision scenarios, which are difficult to test in reality, in particular in a real traffic environment.J. Nilsson ( ) Vehicle

show abstract

Robust Linear Auto-calibration of a Moving Camera from Image Sequences

Cited by 8 publications

References 14 publications

Merging of feature tracks for camera motion estimation from video

Merging of feature tracks for camera motion estimation from video

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

Using Augmentation Techniques for Performance Evaluation in Automotive Safety

Contact Info

Product

Resources

About