Camera auto-calibration from articulated motion

Kuo, Paul; Nebel, Jean‐Christophe; Makris, Dimitrios

doi:10.1109/avss.2007.4425299

Cited by 7 publications

(9 citation statements)

References 9 publications

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“…The method uses a sequence of images captured from the camera and estimates certain points called vanishing points using the line segments in the image. Kuo, Nebel and Makris [14] used the biomechanical constraints of the human body. The algorithm analyses certain key points on the human body during a sequence and detects frames where the body adopts a specific posture which allows for accurate camera calibration.…”

Section: Auto-calibrationmentioning

confidence: 99%

Camera calibration using adaptive segmentation and ellipse fitting for localizing control points

Prakash

Karam

2012

2012 19th IEEE International Conference on Image Processing

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There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets.Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent.This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.ii ACKNOWLEDGEMENTS

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Section: Auto-calibrationmentioning

confidence: 99%

Camera calibration using adaptive segmentation and ellipse fitting for localizing control points

Prakash

Karam

2012

2012 19th IEEE International Conference on Image Processing

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“…These bipedal constraints are much less restricting than assuming a specific type of motions (e.g., walking). In this work, we use such constraints for camera selfcalibration from observing human motion to derive 3D poses for key frames [1] and further infer 3D poses between key frames.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we propose a novel method for estimating a 3D pose from 2D joint locations using a single uncalibrated camera. Assuming 2D positions of these key points have been extracted from a video sequence, we are able to perform camera autocalibration for some key frames automatically selected in the sequence [1]. This exploits a human bipedal motion constraint that certain body joints become coplanar within a motion cycle.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Human Bipedal Motion Constraints for 3d Pose Recovery From a Single Uncalibrated Camera

Kuo

Ammar²,

Lewandowski

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

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A new method is proposed for recovering 3D human poses in video sequences taken from a single uncalibrated camera. This is achieved by exploiting two important constraints observed from human bipedal motion: coplanarity of body key points during the mid-stance position and the presence of a foot on the ground-i.e. static foot-during most activities. Assuming 2D joint locations have been extracted from a video sequence, the algorithm is able to perform camera auto-calibration on specific frames when the human body adopts particular postures. Then, a simplified pin-hole camera model is used to perform 3D pose reconstruction on the calibrated frames. Finally, the static foot constraint which is found in most human bipedal motions is applied to infer body postures for non-calibrated frames. We compared our method with (1) "orthographic reconstruction" method and (2) reconstruction using manually calibrated data. The results validate the assumptions made for the simplified pin-hole camera model and reconstruction results reveal a significant improvement over the orthographic reconstruction method.

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“…Since activity independent techniques have been used to produce 3D posture estimates [8]- [10] and analysis of such sequences allows activity identification [11], initial estimates can be refined using learned motion models. Here, MoCap data of repeated actions provided by the HumanEva dataset [12] are converted into quaternions, which produces sequences of 52-dimension feature vectors.…”

Section: Validation Of Temporal Le Approachmentioning

confidence: 99%

Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series

Lewandowski

Rincón

Makris

et al. 2010

2010 20th International Conference on Pattern Recognition

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Abstract-A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach, temporal information is intrinsic to the objective function, which produces description of low dimensional spaces with time coherence between data points. Since the proposed scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters, it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its lower computational cost and generalisation abilities suggest it is scalable to larger datasets.

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Camera auto-calibration from articulated motion

Cited by 7 publications

References 9 publications

Camera calibration using adaptive segmentation and ellipse fitting for localizing control points

Camera calibration using adaptive segmentation and ellipse fitting for localizing control points

Exploiting Human Bipedal Motion Constraints for 3d Pose Recovery From a Single Uncalibrated Camera

Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series

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